Getty Images uses a managed search system to allow business users to control image search results. The system breaks search scoring into relevancy, recency, and image source components. It provides interfaces to adjust component weights and visualize the effects. Test algorithms can be run on a percentage of users before being promoted to the main search. The system is built on SOLR and uses custom plugins and functions to implement complex scoring and result shuffling while providing business users simple controls.
The document outlines an agenda for a conference on search and recommenders hosted by Lucidworks, including presentations on use cases for ecommerce, compliance, fraud and customer support; a demo of Lucidworks Fusion which leverages signals from user engagement to power both search and recommendations; and a discussion of future directions including ensemble and click-based recommendation approaches.
Learning to Rank in Solr: Presented by Michael Nilsson & Diego Ceccarelli, Bl...Lucidworks
This document summarizes Bloomberg's use of machine learning for search ranking within their Solr implementation. It discusses how they process 8 million searches per day and need machine learning to automatically tune rankings over time as their index grows to 400 million documents. They use a Learning to Rank approach where features are extracted from queries and documents, training data is collected, and a ranking model is generated to optimize metrics like click-through rates. Their Solr Learning to Rank plugin allows this model to re-rank search results in Solr for improved relevance.
Webinar: Solr 6 Deep Dive - SQL and GraphLucidworks
This document provides an agenda and overview for a conference session on Solr 6 and its new capabilities for parallel SQL and graph queries. The session will cover motivations for adding these features to Solr, how streaming expressions enable parallel SQL, graph capabilities through the new graph query parser and streaming expressions, and comparisons to other technologies. The document includes examples of SQL queries and graph streaming expressions in Solr.
Learning to Rank: From Theory to Production - Malvina Josephidou & Diego Cecc...Lucidworks
This document summarizes a presentation about implementing learning-to-rank (LTR) models for search relevance at Bloomberg. It discusses:
1) Collecting query-document judgments and extracting features to train LTR models;
2) Deploying trained models in Solr and applying them to re-rank search results; and
3) Optimizing the system for production use by precomputing static features, using docvalues to retrieve features faster, and evaluating performance.
Relevance in the Wild - Daniel Gomez Vilanueva, FindwiseLucidworks
This document discusses relevance in information retrieval systems. It begins with definitions of relevance and how relevance is measured. It then covers similarity functions like TF-IDF and BM25 that are used to calculate relevance scores. Configuration options for similarity in Solr are presented, including setting similarity globally or per field. The edismax query parser is described along with parameters that impact relevance. Methods for evaluating relevance through testing and analysis are provided. Finally, examples of applying relevance techniques to real systems are briefly outlined.
Measuring Search Engine Quality using Spark and PythonSujit Pal
Presented at PyData Amsterdam 2016. Describes the Rewinder tool, to compare search engine configuration performance between Microsoft FAST and Apache Solr for the ScienceDirect search backend migration.
This document discusses building a data-driven log analysis application using LucidWorks SILK. It begins with an introduction to LucidWorks and discusses the continuum of search capabilities from enterprise search to big data search. It then describes how SILK can enable big data search across structured and unstructured data at massive scale. The solution components involve collecting log data from various sources using connectors, ingesting it into Solr, and building visualizations for analysis. It concludes with a demo and contact information.
Webinar: Replace Google Search Appliance with Lucidworks FusionLucidworks
Lucidworks Senior Search Engineer, Evan Sayer, and Enterprise Content Management and Big Data Architect for the County of Sacramento, Guy Sperry, explore the benefits of replacing Google Search Appliance with Lucidworks Fusion.
Webinar: Fusion for Business IntelligenceLucidworks
Lucidworks Senior Systems Engineer Allan Syiek discusses simple querying vs. data mining and intelligent search, and how Lucidworks Fusion can help you turn raw data into insight.
Let's Build an Inverted Index: Introduction to Apache Lucene/SolrSease
The University Seminar series aim to provide a basic understanding of Open Source Information Retrieval and its application in the real world through the Apache Lucene/Solr technologies.
Creating an end-to-end Recommender System with Apache Spark and Elasticsearch...sparktc
At the sold-out Spark & Machine Learning Meetup in Brussels on October 27, 2016, Nick Pentreath of the Spark Technology Center teamed up with Jean-François Puget of IBM Analytics to deliver a talk called Creating an end-to-endRecommender System with Apache Spark and Elasticsearch.
Jean-François and Nick started with a look at the workflow for recommender systems and machine learning, then moved on to data modeling and using Spark ML for collaborative filtering. They closed with a discussion of deploying and scoring the recommender models, including a demo.
Our Tale from the Trail of Shadows at REI Co-op - Chris Phillips & Dale Smith...Lucidworks
Dale Smith and Chris Phillips replaced REI's SaaS search engine with an on-premise Solr stack to improve search performance and flexibility. They faced challenges of matching the previous relevance tuning and limiting SEO impact. Their solutions included using FindTuner and Fusion for management, a "shadow query" model to compare results from both engines, and normalizing the API. Through a collaborative team approach focused on quality, they launched successfully and saw search times improve by 600ms without customers noticing the change.
What are the main characteristics of E Commerce search and why Apache Solr is one of the best search engines to power ecommerce websites.
Characteristics of E-Commerce Search
Solr: History
Solr: A Brief
Why Solr?
Solr System
Features of Solr
Users
Resources
http://www.thepcwizard.in/p/about-me-and-blog.html
Boosting Documents in Solr by Recency, Popularity and Personal Preferences - ...lucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/revolution/2011
Attendees with come away from this presentation with a good understanding and access to source
code for boosting and/or filtering documents by recency, popularity, and personal preferences. My
solution improves upon the common “recipe” based solution for boosting by document age. The
framework also supports boosting documents by a popularity score, which is calculated and
managed outside the index. I will present a few different ways to calculate popularity in a scalable
manner. Lastly, my solution supports the concept of a personal document collection, where each
user is only interested in a subset of the total number of documents in the index.
Searching and Querying Knowledge Graphs with Solr/SIREn - A Reference Archite...Lucidworks
The document discusses a reference architecture for searching and querying knowledge graphs with Solr/SIREn. It describes challenges in indexing and searching knowledge graphs due to their complex relational structure and diversity of data. The proposed architecture aims to simplify the task by reducing custom code through standardized tools and enabling quick adaptation to changes in data schemas or requirements. Key components include using SPARQL to extract relevant graph subsets and map them to a simplified schema, generating JSON documents from the extracted subgraphs for indexing, and leveraging the SIREn plugin to support structured queries over nested and relational data.
Building a real time big data analytics platform with solrTrey Grainger
Having “big data” is great, but turning that data into actionable intelligence is where the real value lies. This talk will demonstrate how you can use Solr to build a highly scalable data analytics engine to enable customers to engage in lightning fast, real-time knowledge discovery.
At CareerBuilder, we utilize these techniques to report the supply and demand of the labor force, compensation trends, customer performance metrics, and many live internal platform analytics. You will walk away from this talk with an advanced understanding of faceting, including pivot-faceting, geo/radius faceting, time-series faceting, function faceting, and multi-select faceting. You’ll also get a sneak peak at some new faceting capabilities just wrapping up development including distributed pivot facets and percentile/stats faceting, which will be open-sourced.
The presentation will be a technical tutorial, along with real-world use-cases and data visualizations. After this talk, you'll never see Solr as just a text search engine again.
Efficient Scalable Search in a Multi-Tenant Environment: Presented by Harry H...Lucidworks
This document discusses efficient scalable search in a multi-tenant environment. It describes Bloomberg Vault, which hosts large volumes of enterprise communications and documents for compliance. The system uses a distributed architecture with shards that are loaded on demand to serve search queries. Security is ensured by dynamically generating field values that encapsulate access permissions for each user's view of a document.
Real-Time Analytics with Solr: Presented by Yonik Seeley, ClouderaLucidworks
The document describes how Solr can be used for real-time analytics on large datasets. It discusses how Solr's inverted index, columnar storage, and multi-segment indexing enable fast search and analytics. Faceted search is described as a way to break results into buckets to filter and explore the data. The new Solr facet module aims to improve integration, performance, and ease of use for advanced analytics through faceting.
Informational Referential Integrity Constraints Support in Apache Spark with ...Databricks
An informational, or statistical, constraint is a constraint such as a unique, primary key, foreign key, or check constraint that can be used by Apache Spark to improve query performance. Informational constraints are not enforced by the Spark SQL engine; rather, they are used by Catalyst to optimize the query processing. Informational constraints will be primarily targeted to applications that load and analyze data that originated from a data warehouse. For such applications, the conditions for a given constraint are known to be true, so the constraint does not need to be enforced during data load operations.
This session will cover the support for primary and foreign key (referential integrity) constraints in Spark. You’ll learn about the constraint specification, metastore storage, constraint validation and maintenance. You’ll also see examples of query optimizations that utilize referential integrity constraints, such as Join and Distinct elimination and Star Schema detection.
Yaron Inger - Enlight - Inside the app of the year tlv-ios-dev
Presented by Yaron Inger, CTO @Lightricks in the TLV iOS Developers Meetup 23/12/15
http://www.meetup.com/Tel-Aviv-iOS-Developers-Meetup
Enlight is an iOS app designed to be a one-stop-shop for all your photo editing needs, that's been recently selected by Apple as the *2015 App of the Year* in Canada, UK, Germany, China and more, and as runner-up in the US. In this talk I will reveal some of the concepts and infrastructures that enabled us to create this extremely complex app with a relatively small team of designers and developers.
Movie recommendation Engine using Artificial IntelligenceHarivamshi D
My Academic Major Project Movie Recommendation using Artificial Intelligence. We also developed a website named movie engine for the recommendation of movies.
Visualizing Model Selection with Scikit-Yellowbrick: An Introduction to Devel...Benjamin Bengfort
This is an overview of the goals and roadmap for the Yellowbrick model visualization library (www.scikit-yb.org). If you're interested in contributing to Yellowbrick or writing visualizers, this is a good place to get started.
In the presentation we discuss the expected workflow of data scientists interacting with the model selection triple and Scikit-Learn. We describe the Yellowbrick API and it's relationship to the Scikit-Learn API. We introduce our primary object: the Visualizer, an estimator that learns from data and displays it visually. Finally we describe the requirements for developing for Yellowbrick, the tools and utilities in place and how to get started.
Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the Scikit-Learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines Scikit-Learn with Matplotlib in the best tradition of the Scikit-Learn documentation, but to produce visualizations for your models!
This presentation was given during the opening session of the 2017 Spring DDL Research Labs.
The AI-powered employee Appraisal system based on a credit system is a softwa...Chan563583
The AI-powered employee Appraisal system based on a credit system is a software application that aims to provide an efficient and fair way of calculating employee incentives in an organization. The system will use artificial intelligence (AI) algorithms (classification) to analyze employee performance data and assign credits to each employee based on their performance.
The system will work by first defining a set of key performance indicators (KPIs) that are relevant to the organization's goals and objectives. These KPIs could include metrics such as sales revenue, customer satisfaction scores, or project completion rates. Each employee's performance data will then be measured against these KPIs, and the system will assign credits to each employee based on their performance.
The credits assigned to each employee will be used to determine their incentive payout, with higher-performing employees receiving a higher payout. The system will also have the capability to adjust the weight age of different KPIs based on the organization's priorities and objectives.
The classification algorithm used in the system will continuously learn and improve over time, as they are fed more data and feedback from the organization. This will ensure that the system remains relevant and accurate as the organization's goals and objectives evolve.
This document discusses Viewpoint's approach to web API performance testing. It outlines three key checkpoints: (1) ensuring performance during agile sprints through design reviews and trend monitoring, (2) integrating and testing components from different teams, and (3) performing full regression testing before release. It also defines different types of performance testing and describes the tools and processes used, including load testing with Visual Studio, tracking performance metrics, and using dashboards to socialize goals.
The document describes a project to develop a software tool that can generate ratings for individual product features from reviews. It aims to extract key features, determine sentiment ratings for each feature based on reviews, and summarize the ratings. The system collects reviews, segments text, identifies frequent features, determines sentiment orientation of words and sentences, and summarizes opinions for each feature. It was evaluated on accuracy using a benchmark dataset, with results showing reasonable precision and recall levels. Walkthrough examples demonstrate how to use the tool to extract and visualize features ratings from reviews.
How to Automate your Enterprise Application / ERP TestingRTTS
This document discusses automating enterprise application and data warehouse testing using QuerySurge. It begins with an introduction to QuerySurge and its modules for automating data interface testing. These modules allow testing across different data sources with no coding required. The document then covers data maturity models and how QuerySurge can help improve testing processes. It demonstrates how QuerySurge can automate testing to gain full coverage while decreasing testing time. In conclusion, it discusses how QuerySurge provides value through increased testing efficiency and data quality.
What is Rated Ranking Evaluator and how to use it (for both Software Engineer and IT Manager). Talk made during Chorus Workshops at Plainschwarz Salon.
The document provides an overview of software test automation and testing frameworks. It discusses:
- What test automation is and where it fits in the software development life cycle. Key benefits include increasing test coverage, reducing manual testing, and enabling regression testing.
- What testing frameworks are and why they are needed. Frameworks provide standardized environments for executing automated tests and reporting results.
- The main types of testing frameworks: modular, data-driven, and keyword-driven. Data-driven frameworks separate test data from test scripts for increased flexibility.
- A shift from traditional waterfall models to more agile development approaches and the rise of test-driven development and behavior-driven development.
Architecting AI Solutions in Azure for BusinessIvo Andreev
The topic is about Azure solution architectures that involve IoT and AI to solve common business domain problems. With near real time recommender system and an object detection with image recognition we review the architecture, build from the ground-up and illustrate how the typical realistic challenges could be addressed.
The document discusses a software estimation challenge hosted at the Nesma Conference 2020. It provides context about the conference and challenge, describes the inputs, tasks, and deliverables of the challenge. It then details Metri's approach to completing the tasks, which included estimating the functional size using various methods, estimating the impact of non-functional requirements, and using historical project data to estimate the effort required to develop the software.
IRJET - Online Product Scoring based on Sentiment based Review AnalysisIRJET Journal
The document proposes a system to analyze online product reviews and assign an authenticity score to products based on sentiment analysis of the reviews using a Support Vector Machine (SVM) classifier. It discusses how sentiment analysis can help classify genuine reviews from fake or agenda-driven reviews. The proposed system would scrape product reviews, preprocess and vectorize the text, classify the sentiment, and provide an overall product rating to help users determine if they want to purchase a product.
Testing frameworks provide an execution environment for automated tests. The main types are modular, data-driven, and keyword-driven frameworks. Modular frameworks organize tests into independent scripts representing application modules. Data-driven frameworks store test data and expected results in external files to reduce code duplication. Keyword-driven frameworks use external files to store test actions and data. Hybrid frameworks combine advantages of the different approaches. While frameworks work with waterfall models, agile methodologies benefit more from test-driven development and behavior-driven development which integrate testing throughout development.
IRJET- Hybrid Recommendation System for MoviesIRJET Journal
This document describes a hybrid recommendation system for movies that combines collaborative and content-based filtering. It uses the MovieLens rating dataset and supplements it with additional data from IMDB, such as movie details. Algorithms like nearest neighbors collaborative filtering and content-based filtering are used to provide personalized movie recommendations to users. The system architecture and design are outlined, including user profiles, movie searching, and success prediction for upcoming movies. An evaluation of the system demonstrates how additional content features can improve recommendation accuracy over collaborative filtering alone.
This document aims to classify Amazon product reviews using sentiment analysis. It introduces text classification and describes the dataset, data processing steps, and classification system model used, which includes feature extraction, selection, and classification algorithms like KNN, Random Forest, and SVM. The document also discusses evaluation metrics, applications, results, and concludes that the system can help retailers analyze customer sentiment to improve their products.
The document describes a presentation for a project called Promostat, which aims to provide an online advertising marketplace. Some key points:
- Promostat allows advertisers and publishers to register on a central portal to access the system and promote products online.
- The proposed system provides a more user-friendly and secure registration process compared to the existing manual system.
- The presentation covers the system requirements, modules, design, implementation, testing and outputs.
- Modules include administration, registration, publisher functions, and displaying promos. Testing involved unit, integration and system testing.
Neev uses a scrum based Agile Development methodology, a proven Extended Delivery Center model of engagement - all designed to ensure high quality, timely deliverables.
5 Key Metrics to Release Better Software FasterDynatrace
No matter how often you deploy your application, or how sophisticated your delivery pipeline is, you always need to know the quality status of the software you are building. This can only be done if you measure it. But measure what exactly?
Andreas Grabner and Brett Hofer, app performance evangelists, explain five key metrics to increase your confidence in securing a safe build for production.
• Learn why metrics can be huge quality gateways
• Identify key metrics to take back to your team (Dev, Test, Ops and Business)
• Understand how to use, measure and report these metrics
• 3 short use cases and how using metrics can help you avoid them
The document describes the architecture of Galene, LinkedIn's search stack. It consists of a frontend, backend, federator, brokers, and search verticals. The federator routes queries to brokers which then query searchers to retrieve results from indexes. Results are merged, blended and returned. Live updates are supported through incremental updates to segmented indexes while maintaining a static rank ordering.
Similar to Managed Search: Presented by Jacob Graves, Getty Images (20)
Search is the Tip of the Spear for Your B2B eCommerce StrategyLucidworks
With ecommerce experiencing explosive growth, it seems intuitive that the B2B segment of that ecosystem is mirroring the same trajectory. That said, B2B has very different needs when it comes to transacting with the same style of experiences that we see in B2C. For instance, B2B ecommerce is about precision findability, whereas B2C customers can convert at higher rates when they’re just browsing online. In order for the B2B buying experience to be successful, search needs to be tuned to meet the unique needs of the segment.
In this webinar with Forrester senior analyst Joe Cicman, you’ll learn:
-Which verticals in B2B will drive the most growth, and how machine-learning powered personalization tactics can be deployed to support those specific verticals
-Why an omnichannel selling approach must be deployed in order to see success in B2B
-How deploying content search capabilities will support a longer sales cycle at scale
-What the next steps are to support a robust B2B commerce strategy supported by new technology
Speakers
Joe Cicman, Senior Analyst, Forrester
Jenny Gomez, VP of Marketing, Lucidworks
Customer loyalty starts with quickly responding to your customer’s needs. When it comes to resolving open support cases, time is of the essence. Time spent searching for answers adds up and creates inefficiencies in resolving cases at scale. Relevant answers need to be a few clicks away and easily accessible for agents directly from their service console.
We will explore how Lucidworks’ Agent Insights application automatically connects agents with the correct answers and resources. You’ll learn how to:
-Configure a proactive widget in an agent’s case view page to access resources across third-party systems (such as Sharepoint, Confluence, JIRA, Zendesk, and ServiceNow).
-Easily set up query pipelines to autonomously route assets and resources that are relevant to the case-at-hand—directly to the right agent.
-Identify subject matter experts within your support data and access tribal knowledge with lightning-fast speed.
How Crate & Barrel Connects Shoppers with Relevant ProductsLucidworks
Lunch and Learn during Retail TouchPoints #RIC21 virtual event.
***
Crate & Barrel’s previous search solution couldn’t provide its shoppers with an online search and browse experience consistent with the customer-centric Crate & Barrel brand. Meanwhile, Crate & Barrel merchandisers spent the bulk of their time manually creating and maintaining search rules. The search experience impacted customer retention, loyalty, and revenue growth.
Join this lunch & learn for an interactive chat on how Crate & Barrel partnered with Lucidworks to:
-Improve search and browse by modernizing the technology stack with ML-based personalization and merchandising solutions
-Enhance the experience for both shoppers and merchandisers
-Explore signals to transform the omnichannel shopping experience
Questions? Visit https://lucidworks.com/contact/
Learn how to guide customers to relevant products using eCommerce search, hyper-personalisation, and recommendations in our ‘Best-In-Class Retail Product Discovery’ webinar.
Nowadays, shoppers want their online experience to be engaging, inspirational and fulfilling. They want to find what they’re looking for quickly and easily. If the sought after item isn’t available, they want the next best product or content surfaced to them. They want a website to understand their goals as though they were talking to a sales assistant in person, in-store.
In this webinar, we explore IMRG industry data insights and a best-in-class example of retail product discovery. You’ll learn:
- How AI can drive increased revenue through hyper-personalised experiences
- How user intent can be easily understood and results displayed immediately
- How merchandisers can be empowered to curate results and product placement – all without having to rely on IT.
Presented by:
Dave Hawkins, Principal Sales Engineer - Lucidworks
Matthew Walsh, Director of Data & Retail - IMRG
Connected Experiences Are Personalized ExperiencesLucidworks
Many companies claim personalization and omnichannel capabilities are top priorities. Few are able to deliver on those experiences.
For a recent Lucidworks-commissioned study, Forrester Consulting surveyed 350+ global business decision-makers to see what gets in the way of achieving these goals. They discovered that inefficient technology, lack of behavioral insights, and failure to tie initiatives to enterprise-wide goals are some of the most frequent blockers to personalization success.
Join guest speaker, Forrester VP and Principal Analyst, Brendan Witcher, and Lucidworks CEO, Will Hayes, to hear the results of the Forrester Consulting study, how to avoid “digital blindness,” and how to apply VoC data in real-time to delight customers with personalized experiences connected across every touchpoint.
In this webinar, you’ll learn:
- Why companies who utilize real-time customer signals report more effective personalization
- How to connect employees and customers in a shared experience through search and browse
- How Lucidworks clients Lenovo, Morgan Stanley and Red Hat fast-tracked improvements in conversion, engagement and customer satisfaction
Featuring
- Will Hayes, CEO, Lucidworks
- Brendan Witcher, VP, Principal Analyst, Forrester
Intelligent Insight Driven Policing with MC+A, Toronto Police Service and Luc...Lucidworks
Intelligent Policing. Leveraging Data to more effectively Serve Communities.
Policing in the next decade is anticipated to be very different from historical methods. More data driven, more focused on the intricacies of communities they serve and more open and collaborative to make informed recommendations a reality. Whether its social populations, NIBRS or organization improvement that’s the driver, the IT requirement is largely the same. Provide 360 access to large volumes of siloed data to gain a full 360 understanding of existing connections and patterns for improved insight and recommendation.
Join us for a round table discussion of how the Toronto Police Service is better serving their community through deploying a unified intelligent data platform.
Data innovation improves officers' engagement with existing data and streamlines investigation workflows by enhancing collaboration. This improved visibility into existing police data allows for a more intelligent and responsive police force.
In this webinar, we'll cover:
-The technology needs of an intelligent police force.
-How a Global Search improves an officer's interaction with existing data.
Featuring:
-Simon Taylor, VP, Worldwide Channels & Alliances, Lucidworks
-Michael Cizmar, Managing Director, MC+A
-Ian Williams, Manager of Analytics & Innovation, Toronto Police Service
[Webinar] Intelligent Policing. Leveraging Data to more effectively Serve Com...Lucidworks
Policing in the next decade is anticipated to be very different from historical methods. More data driven, more focused on the intricacies of communities they serve and more open and collaborative to make informed recommendations a reality. Whether its social populations, NIBRS or organization improvement that’s the driver, the IT requirement is largely the same. Provide 360 access to large volumes of siloed data to gain a full 360 understanding of existing connections and patterns for improved insight and recommendation.
Join us for a round table discussion of how the Toronto Police Service is better serving their community through deploying a unified intelligent data platform.
Data innovation improves officers' engagement with existing data and streamlines investigation workflows by enhancing collaboration. This improved visibility into existing police data allows for a more intelligent and responsive police force.
In this webinar, we'll cover:
The technology needs of an intelligent police force.
How a Global Search improves an officer's interaction with existing data.
Featuring
-Simon Taylor, VP, Worldwide Channels & Alliances, Lucidworks
-Michael Cizmar, Managing Director, MC+A
-Ian Williams, Manager of Analytics & Innovation, Toronto Police Service
Preparing for Peak in Ecommerce | eTail Asia 2020Lucidworks
This document provides a framework for prioritizing onsite search problems and key performance indicators (KPIs) to measure for e-commerce search optimization. It recommends prioritizing fixing searches that yield no results, improving relevance of results, and reducing false positives. The most essential KPIs to measure include query latency, throughput, result relevance through click-through rates and NDCG scores. The document also provides tips for self-benchmarking search performance and examples of search performance benchmarks across nine e-commerce sites from various industries.
Accelerate The Path To Purchase With Product Discovery at Retail Innovation C...Lucidworks
Wish your conversion rates were higher? Can’t figure out how to efficiently and effectively serve all the visitors on your site? Embarrassed by the quality of your product discovery experience? The bar is high and the influx of online shopping over recent months has reminded us that the opportunities are real. We’re all deep in holiday prep, but let’s take a few minutes to think about January 2021 and beyond. How can we position ourselves for success with our customers and against our competition?
Grab your lunch and let’s dive into three strategies that need to be part of your 2021 roadmap. You don’t need an army to get there. But you do need to take action and capitalize on the shoppers abandoning the product discovery journey on your site.
In this session, attendees will find out how to:
-Take control of merchandising at scale;
-Implement hands-free search relevancy; and
-Address personalization challenges.
AI-Powered Linguistics and Search with Fusion and RosetteLucidworks
For a personalized search experience, search curation requires robust text interpretation, data enrichment, relevancy tuning and recommendations. In order to achieve this, language and entity identification are crucial.
For teams working on search applications, advanced language packages allow them to achieve greater recall without sacrificing precision.
Join us for a guided tour of our new Advanced Linguistics packages, available in Fusion, thanks to the technology partnership between Lucidworks and Basistech.
We’ll explore the application of language identification and entity extraction in the context of search, along with practical examples of personalizing search and enhancing entity extraction.
In this webinar, we’ll cover:
-How Fusion uses the Rosette Basic Linguistics and Entity Extraction packages
-Tips for improving language identification and treatment as well as data enrichment for personalization
-Speech2 demo modeling Active Recommendation
-Use Rosette’s packages with Fusion Pipelines to build custom entities for specific domain use cases
Featuring:
-Radu Miclaus, Director of Product, AI and Cloud, Lucidworks, Lucidworks
-Robert Lucarini, Senior Software Engineer, Lucidworks
-Nick Belanger, Solutions Engineer, Basis Technology
The Service Industry After COVID-19: The Soul of Service in a Virtual MomentLucidworks
Before COVID-19, almost 80% of the US workforce worked service in jobs that involve in-person interaction with strangers. Now, leaders of service organizations must reshape their offerings during the pandemic and prepare for whatever the new normal turns out to be. Our three panelists will share ideas for adapting their service businesses, now that closer-than-six-feet isn’t an option.
Join Lucidworks as we talk shop with 3 service business leaders, covering:
-Common impacts of the pandemic on service businesses (and what to do about them),
-How service teams can maintain a human touch across virtual channels, and
-Plans for the future, before and after the pandemic subsides.
Featuring
-Sara Nathan, President & CEO, AMIGOS
-Anthony Carruesco, Founder, AC Fly Fishing
-sara bradley, chef and proprietor, freight house
-Justin Sears, VP Product Marketing, Lucidworks
Webinar: Smart answers for employee and customer support after covid 19 - EuropeLucidworks
The COVID-19 pandemic has forced companies to support far more customers and employees through digital channels than ever before. Many are turning to chatbots to help meet increasing demand, but traditional rules-based approaches can’t keep up. Our new Smart Answers add-on to Lucidworks Fusion makes existing chatbots and virtual assistants more intelligent and more valuable to the people you serve.
Smart Answers for Employee and Customer Support After COVID-19Lucidworks
Watch our on-demand webinar showcasing Smart Answers on Lucidworks Fusion. This technology makes existing chatbots and virtual assistants more intelligent and more valuable to the people you serve.
In this webinar, we’ll cover off:
-How search and deep learning extend conversational frameworks for improved experiences
-How Smart Answers improves customer care, call deflection, and employee self-service
-A live demo of Smart Answers for multi-channel self-service support
Applying AI & Search in Europe - featuring 451 ResearchLucidworks
In the current climate, it’s now more important than ever to digitally enable your workforce and customers.
Hear from Simon Taylor, VP Global Partners & Alliances, Lucidworks and Matt Aslett, Research Vice President, 451 Research to get the inside scoop on how industry leaders in Europe are developing and executing their digital transformation strategies.
In this webinar, we’ll discuss:
The top challenges and aspirations European business and technology leaders are solving using AI and search technology
Which search and AI use cases are making the biggest impact in industries such as finance, healthcare, retail and energy in Europe
What technology buyers should look for when evaluating AI and search solutions
Webinar: Accelerate Data Science with Fusion 5.1Lucidworks
This document introduces Fusion 5.1 and its new capabilities for integrating with data science tools like Tensorflow, Scikit-Learn, and Spacy.
It provides an overview of Fusion's capabilities for understanding content, users, and delivering insights at scale. The document then demonstrates Fusion's Jupyter Notebook integration for reading and writing data and running SQL queries.
Finally, it shows how Fusion integrates with Seldon Core to easily deploy machine learning models with tools like Tensorflow and Scikit-Learn. A live demo is provided of deploying a custom model and using it in Fusion's query and indexing pipelines.
Webinar: 5 Must-Have Items You Need for Your 2020 Ecommerce StrategyLucidworks
In this webinar with 451 Research, you'll understand how retailers are using AI to predict customer intent and learn which key performance metrics are used by more than 120 online retailers in Lucidworks’ 2019 Retail Benchmark Survey.
In this webinar, you’ll learn:
● What trends and opportunities are facing the ecommerce industry in 2020
● Why search is the universal path to understanding customer intent
● How large online retailers apply AI to maximize the effectiveness of their personalization efforts
Where Search Meets Science and Style Meets Savings: Nordstrom Rack's Journey ...Lucidworks
Nordstrom Rack | Hautelook curates and serves customers a wide selection of on-trend apparel, accessories, and shoes at an everyday savings of up to 75 percent off regular prices. With over a million visitors shopping across different platforms every day, and a realization that customers have become accustomed to robust and personalized search interactions, Nordstrom Rack | Hautelook launched an initiative over a year ago to provide data science-driven digital experiences to their customers.
In this session, we’ll discuss Nordstrom Rack | Hautelook’s journey of operationalizing a hefty strategy, optimizing a fickle infrastructure, and rallying troops around a single vision of building an expansible machine-learning driven product discovery engine.
The audience will learn about:
-The key technical challenges and outcomes that come with onboarding a solution
-The lessons learned of creating and executing operational design
-The use of Lucidworks Fusion to plug custom data science models into search and browse applications to understand user intent and deliver personalized experiences
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Managed Search: Presented by Jacob Graves, Getty Images
2. Managed Search
Jacob Graves,
Principal Engineer at Getty Images
jacob.graves@gettyimages.com
3. Introduction
Getty Images is the global leader in visual communications with over 170 million
assets available through its premium content site www.gettyimages.com and its
leading stock content site www.istock.com. With its advanced search and image
recognition technology, Getty Images serves business customers in more than 100
countries and is the first place creative and media professionals turn to discover,
purchase and manage images and other digital content. Its award-winning
photographers and content creators help customers produce inspiring work which
appears every day in the world’s most influential newspapers, magazines, advertising
campaigns, films, television programs, books and online media.
4. Getty Search
Obviously, in order to buy images you have to be able to find them.
Search Process:
• Receive search containing words.
• Tokenize and map the words onto our controlled vocabulary keywords
• Find all the images associated with the correct keywords.
• Score all the images and then sort them by the score.
The scoring determines which images the users see.
5. Managed Search
The details of how the scoring takes place is a technical concern, but the end result is
a business concern.
Goal – make business users self sufficient.
So the problem is to create a framework for business users that will:
• Hide technical complexity.
• Allows Control over scoring components and result ordering.
• Allows Balancing of these scoring components against each other.
• Provides Feedback.
• Allows Visualization of the results of their changes.
We call this Managed Search.
6. Managed Search – Our Implementation
1. We created a SOLR search ecosystem containing all our images, keywords and
associated metadata, and added plugins using Java.
2. We used a C# middle tier to wrap around our SOLR ecosystem.
3. We built a web application called SAW – Search Administration Workbench, using
the Java Play framework and lots of javascript.
7. Managed Search Architecture Diagram
SOLR
Custom
functions
(valuesources)
Price
Tier
Shuffle
(RankQuery)
Index
settings
with
debug
scores
SOLR
select
url Save
Search
Middle
Tier
SAW
Site
Business
User
Customer
Algorithm
DB
Search
and
algorithm
Search
results
Load
algorithm
settings
search
Search
results
algorithm
settings
Search
results
(for
site
searches)
8. SAW
SAW has 5 main areas:
• Algorithm – control sort scoring.
• Preview – see search results.
• Single Page Charts – single search score component charts.
• Scale report charts – all searches score component charts.
• Live tests – expose test algorithms to live users to gather and view KPI data.
9. Scoring Breakdown
To help the business control the scoring we break it down into 3 different scoring
components:
• Relevancy – image attributes that are relative to the search (i.e. keywords).
• Recency
• Image Source – image attributes that are not related to the specific search.
Then we provide 2 types of parameter the user can control:
• Internal parameters - to control how the component is calculated.
• External boosts - to control how the components are weighted against each other
11. Scoring Architecture
• In order to allow immediate feedback we have to implement scoring using query
time boosting.
• Use boost functions as they are cleaner.
• Favor Query time over Index time, to prioritize control over small performance
gains.
• Define minimum performance metrics and ensure that we stay within them.
Initially we had concentrated on performance above all else and had ended up with
inflexible scoring in return for fairly minor performance gains.
We used the Valuesource plugin to create our own boost functions.
12. Relevancy
• The most important component, how confident are we that this image is correct?
• We measure relevancy at the image/keyword level by tracking user interactions.
• After experimenting we settled on a form of the standard tf-idf VSM (Vector Space
Model) and expose a normalization parameter.
• We also expose a boost so they can control the strength of relevancy relative to
other factors
13. Recency
• Recency is the age of the images.
• Newer images get a higher score to prevent staleness.
• Aging curve – the way an images recency score changes with age.
• We expose 3 different aging curves (reciprocal, linear and reversed reciprocal) and
appropriate parameters to control the shape of the curve.
• We also expose a boost so they can control the strength of recency relative to
other factors
15. Image Source
• We have a variety of image level attribute data that should affect the sort order,
mostly to do with how likely we think the image is to be of high quality.
• We separate our images into groups based on these attributes, called the source.
• We expose a boost that allows the users to increase the score of images with a
given source.
• Unlike relevancy, this is an image level, not image/keyword level property, so it
doesn’t vary from one search to the next.
• Because it isn’t context specific it is dangerous to make this boost too large.
16. Custom Shuffle
As well as influencing the scoring, the business wants to have control over the order
the images where displayed in, so that instead of just appearing in score order certain
slots on the page can be allocated to particular classes of image. This is to ensure
that we always show a diverse range of images.
To accommodate this we need to be able to apply a custom shuffle, similar to a sort
but with more control. To accomplish this we take advantage of a new SOLR plugin
(new in 4.9) called the RankQuery plugin.
17. Image Tier Shuffle
We classify our images into separate groups or image tiers based on various image
level attributes, e.g.
• Licensing Structure
• Image partner
• Exclusivity
• Etc.
We distill these factors into a single image property that we assigned at index time.
We generate a mapping of result slots to image tiers, e.g.
• slot 1 => image tier 2
• slot 2 => image tier 4
• etc.
We pass in the mapping at query time and used the RankQuery implementation to shuffle
the query results.
18. Preview page
• Search and get results scored using algorithm settings.
• Display in pages of 100 images.
• Show image score breakdown by component.
• Show image tier.
To calculate the score for each component we run the SOLR query in debug mode,
and parse the results with regex expressions to get the score for each component.
This is the least stable piece of the whole application, as debug syntax can change
quite frequently between SOLR releases. However, it’s also pretty easy to fix.
20. Single Page Charts
This allows the users to verify what they think they are seeing visually with numbers.
• Aggregate the component scoring data across all the 100 images on a page.
• Create interactive charts from the data.
• Charts that display the distribution of each score component.
• Chart that displays the comparative score from each component.
• Chart that shows the custom shuffle distribution.
We use the javascript D3.js library to generate the graphs.
22. Scale Reports
This allows the users to validate their settings across the full spectrum of searches
that users execute at Getty.
• Execute 1000 different searches (throttled).
• Use the first 100 images from each search by default, number can be increased up
to 10000 (slower).
• Aggregate the component scoring data across all the results.
• Create and display charts similar to the ones used in the single page charts view.
To generate the list of 1000 searches we use proportional sampling, from search log
data.
24. Live tests
Once the users are happy with an algorithm the next stage is to test it for real.
To do this we have a page that controls:
• The algorithm settings for the various live and test sorts.
• Saving these settings to a database where they are used to generate production
SOLR queries.
• The percentage of users for a given live sort that will be allocated to a test sort.
25. KPI monitoring
We also have a page that displays the user interaction data.
• Displays actions against our KPI’s (Key Performance Indicators).
• Primarily we use click-through (i.e. user clicks on an image in the search results).
• Broken out by time and by sort so we can compare the test algorithms against the
live ones.
• We get this data in a feed from our existing analytics framework.
26. Conclusion
Self sufficient business user, path to changing sort order:
1. Change algorithm settings.
2. Execute searches and evaluate sort order visually.
3. Use single page charts to confirm visual impressions.
4. Use scale report to confirm behavior across proportional set of searches.
5. Set a test algorithm to have the settings you want.
6. Set a percentage of users to experience the test.
7. Monitor KPI’s over time to see if settings work as intended.
8. Set the live algorithm to have the settings you want.
27. ValueSource Plugins
This is a well-established SOLR plugin for adding custom query functions.
http://wiki.apache.org/solr/SolrPlugins#ValueSourceParser
There are 3 parts:
• Implement ValueSource. This is where the actual logic is implemented. It can take in
either simple datatypes (like Strings or floats) or other ValueSource objects (e.g. an asset
field value or another query function).
• Implement ValueSourceParser. This creates the ValueSource object with appropriate
inputs.
• Solrconfig.xml. Add a line to enable the new ValueSource plugin.
You can look at any of the existing Query function implementations to see how they should
work.
e.g. – for the “Map” query function see:
• org.apache.solr.search.ValueSourceParser
• org.apache.lucene.queries.function.valuesource.RangeMapFloatFunction
You can also change the debug output so that we can see the results of each custom
function in debug mode, this allows us to display the individual score components to the
users.
28. RankQuery Implementation
This is a new plugin in SOLR 4.9, created by Joel Bernstein.
https://issues.apache.org/jira/browse/SOLR-5973
There is a test in the SOLR 4.9 tests that shows a good example implementation:
org.apache.solr.search.TestRankQueryPlugin
Very briefly, you have to implement:
• QParserPlugin, it creates and returns the QParser implementation.
• QParser, it creates and returns the RankQuery implementation.
• RankQuery, it creates and returns the TopDocsCollector and MergeStrategy implementations.
• TopDocsCollector, this returns the top documents from each shard that you wish to include in your final
results. In our case we separate the documents into separate priority queues by image tier, and order by
score within each image tier. Then we go through a pre-determined list of which image tier should occupy
each slot, and pull the next item from the appropriate image tier priority queue to generate the top
documents List.
• MergeStrategy, this combines the top documents generated by the TopDocsCollectors on each shard. In
our case we followed the same logic as we had for each individual shard, assigning documents to priority
queues by image tier in score order, and then assigning queues to pre-determined slots.
Lastly you reference the new QParserPlugin in your solrconfig.xml.
The pre-determined list of image tier slots could either be a user configurable parameter that is passed in or it
could just included in the solrconfig.xml, or even hard coded.
29. Q & A
Please contact me if you have any questions or thoughts.
I will be attending till the end of the conference.
Email – jacob.graves@gettyimages.com