This document provides an overview of key concepts in recommender systems, including:
- The components of a recommender system including users, items, interactions, features, representations, predictions, loss functions, and learning.
- Design considerations for recommender systems such as choosing appropriate interaction values, features, representation functions, prediction functions, and loss functions.
- Examples of different types of recommender systems including collaborative filtering, content-based, hybrid, and real-world systems from Netflix, YouTube, and e-commerce.
- Tools for building recommender systems in Python like Implicit, Scikit-Learn, LightFM, TensorRec, and Annoy.
The document discusses the architecture
basic Function and Terminology of Recommendation Systems. Some Algorithmic Implementation with some sample Dataset for Understanding. It contains all the Layers of RS Framework well explained.
This document proposes a calibrated recommendations approach that aims to provide recommendations that reflect all of a user's interests in correct proportions. Standard recommender systems trained for accuracy can lead to unbalanced recommendations that amplify a user's main interests and crowd out lesser interests. The calibrated recommendations approach uses a post-processing re-ranking step to optimize a submodular calibration metric, balancing accuracy and fairness by recommending items from all a user's interests in their correct proportions. Experiments on MovieLens data show that calibration can be improved significantly without degrading accuracy much.
Recommender systems are software agents that analyze a user's preferences through transactions and provide personalized recommendations accordingly. There are several recommendation paradigms including non-personalized rules, personalized rules based on user data, and transaction-based collaborative filtering that learns from user interactions. Context-based recommender systems also consider additional information like time, location, or device to provide adaptive recommendations. Common techniques used in recommender systems include content-based filtering that recommends similar items, collaborative filtering that finds users with similar tastes, and demographic-based recommendations.
This document provides an overview of recommender systems for e-commerce. It discusses various recommender approaches including collaborative filtering algorithms like nearest neighbor methods, item-based collaborative filtering, and matrix factorization. It also covers content-based recommendation, classification techniques, addressing challenges like data sparsity and scalability, and hybrid recommendation approaches.
Past, present, and future of Recommender Systems: an industry perspectiveXavier Amatriain
Keynote for the ACM Intelligent User Interface conference in 2016 in Sonoma, CA. I start with the past by talking about the Recommender Problem, and the Netflix Prize. Then I go into the Present and the Future by talking about approaches that go beyond rating prediction and ranking and by finishing with some of the most important lessons learned over the years. Throughout my talk I put special emphasis on the relation between algorithms and the User Interface.
Overview of the Recommender system or recommendation system. RFM Concepts in brief. Collaborative Filtering in Item and User based. Content-based Recommendation also described.Product Association Recommender System. Stereotype Recommendation described with advantage and limitations.Customer Lifetime. Recommender System Analysis and Solving Cycle.
Recommender systems using collaborative filteringD Yogendra Rao
This document summarizes a student project on implementing recommender systems. The project objectives were to design a website using user-based, item-based, and model-based collaborative filtering as well as MapReduce to generate movie recommendations. The system was tested on the MovieLens dataset using MAE and RMSE metrics, with user-based filtering found to have the best performance. The document outlines the technical aspects of the recommendation system including the technologies used, website architecture, and references.
Past, Present & Future of Recommender Systems: An Industry PerspectiveJustin Basilico
Slides from our talk at the RecSys 2016 conference in Boston, MA 2016-09-18 on our perspective for what are important areas for future work in recommender systems.
The document provides an overview of recommender systems. It discusses the typical architecture of recommender systems and describes three main types: collaborative filtering systems, content-based systems, and knowledge-based systems. It also covers paradigms like collaborative filtering, content-based, knowledge-based, and hybrid recommender systems. The document then focuses on collaborative filtering techniques like user-based nearest neighbor collaborative filtering and item-based collaborative filtering. It also discusses latent factor models, matrix factorization approaches, and context-based recommender systems.
Collaborative filtering is a technique used in recommender systems to predict a user's preferences based on other similar users' preferences. It involves collecting ratings data from users, calculating similarities between users or items, and making recommendations. Common approaches include user-user collaborative filtering, item-item collaborative filtering, and probabilistic matrix factorization. Recommender systems are evaluated both offline using metrics like MAE and RMSE, and through online user testing.
What really are recommendations engines nowadays?
This presentation introduces the foundations of recommendation algorithms, and covers common approaches as well as some of the most advanced techniques. Although more focused on efficiency than theoretical properties, basics of matrix algebra and optimization-based machine learning are used through the presentation.
Table of Contents:
1. Collaborative Filtering
1.1 User-User
1.2 Item-Item
1.3 User-Item
* Matrix Factorization
* Stochastic Gradient Descent (SGD)
* Truncated Singular Value Decomposition (SVD)
* Alternating Least Square (ALS)
* Deep Learning
2. Content Extraction
* Item-Item Similarities
* Deep Content Extraction: NLP, CNN, LSTM
3. Hybrid Models
4. In Production
4.1 Problematics
4.2 Solutions
4.3 Tools
(Presented at the Deep Learning Re-Work SF Summit on 01/25/2018)
In this talk, we go through the traditional recommendation systems set-up, and show that deep learning approaches in that set-up don't bring a lot of extra value. We then focus on different ways to leverage these techniques, most of which relying on breaking away from that traditional set-up; through providing additional data to your recommendation algorithm, modeling different facets of user/item interactions, and most importantly re-framing the recommendation problem itself. In particular we show a few results obtained by casting the problem as a contextual sequence prediction task, and using it to model time (a very important dimension in most recommendation systems).
This document discusses recommendation systems and provides examples of different types of recommendation approaches. It introduces collaborative filtering and content-based filtering as the main recommendation techniques. For collaborative filtering, it provides an example of item-based collaborative filtering using the R programming language on a Last.fm music dataset. Content-based filtering recommends items based on their properties and features. Hybrid systems combine collaborative and content-based filtering to generate recommendations.
Presentation at the Netflix Expo session at RecSys 2020 virtual conference on 2020-09-24. It provides an overview of recommendation and personalization at Netflix and then highlights some of the things we’ve been working on as well as some important open research questions in the field of recommendations.
The document discusses recommender systems and describes several techniques used in collaborative filtering recommender systems including k-nearest neighbors (kNN), singular value decomposition (SVD), and similarity weights optimization (SWO). It provides examples of how these techniques work and compares kNN to SWO. The document aims to explain state-of-the-art recommender system methods.
This document discusses content-based recommendation techniques. It explains that content-based recommendation systems learn a user's preferences based on item attributes and characteristics to recommend similar items. It describes representing items and user profiles as vectors of keywords and computing similarity using metrics like cosine similarity. Finally, it briefly outlines probabilistic recommendation methods and linear classifiers for recommendations.
Tutorial: Context In Recommender SystemsYONG ZHENG
This document provides an overview of a tutorial on context-aware recommender systems. The tutorial will cover traditional recommendation techniques, context-aware recommendation which incorporates additional contextual information such as time and location, and context suggestion. It includes an agenda with topics, background information on recommender systems and evaluation metrics, and descriptions of techniques for context-aware recommendation including context filtering and modeling.
Movie Recommender System Using Artificial Intelligence Shrutika Oswal
In recent years, a huge amount of information is available on the internet and it is very difficult for the user to collect the relevant information. While purchasing any product also a lot of choices available and the user is confused about what to choose. This will be a time-consuming process as well. The search engine will solve this problem to some extent by but it will fail in giving a personalized recommendation. In this presentation, I will describe the different types and working of the recommender system how they gather the data, build recommender, generate recommendations from it, evaluate the performance and effectiveness of the recommender system. The further part of the presentation will describe how to build a movie recommender system using python.
Lectures for Masterclass Customer Experience Strategie & Executie @Business University Nyenrode
user/customer centric design principes voor digital touchpoints & Usability & user experience principes
The document discusses conceptualizing personalization in information architecture. It covers personalization strategies, scenarios, profiles, use cases, rules, and architectural requirements. Personalization is modeled using various techniques like conceptual models, relationships diagrams, aspects-oriented use cases, and rules to specify conditions for personalization. Tracking user behavior over time and probabilistic models are discussed to enable adaptive interfaces. The key is to separately describe user experiences with and without personalization and align personalization with business goals.
This document discusses user-centered design (UCD) and usability testing. It defines UCD as a process that systematically researches end users' needs and incorporates them into each stage of design. Usability is defined as how effectively, efficiently, and satisfactorily users can complete tasks. The document outlines UCD activities like requirements analysis, design, development, and testing. It discusses benefits of UCD like eliminating assumptions and validating the product with users. Finally, it provides examples of usability metrics and sources for healthcare usability documentation.
1. The document discusses responsible AI and outlines several principles for developing AI systems responsibly, including privacy, fairness, transparency, reliability, inclusiveness, and accountability.
2. It provides examples of techniques like differential privacy and model constraints that can help mitigate privacy and fairness issues in AI systems.
3. The document also discusses the importance of transparency in AI through explainability, highlighting packages and methods for interpreting models.
IRJET- Opinion Mining and Sentiment Analysis for Online ReviewIRJET Journal
This document summarizes a research paper that proposes a system for conducting sentiment analysis on online product reviews. The system uses a dual sentiment analysis approach that trains a classifier on both original reviews and sentiment-reversed reviews to address issues with polarity shifts. It generates random keys for users to access the review system and uses clustering algorithms to differentiate positive and negative words in reviews and provide an overall product rating. The goal is to help users make more informed purchasing decisions based on genuine reviews by preventing fake reviews from improperly influencing ratings.
A Novel Jewellery Recommendation System using Machine Learning and Natural La...IRJET Journal
This document discusses a novel jewelry recommendation system using machine learning and natural language processing. It proposes both a collaborative model based on user ratings and item popularity, and a hybrid model combining sentiment analysis with machine learning. For the collaborative model, singular value decomposition is used to reduce the dimensionality of large user-item rating matrices. The hybrid model performs sentiment classification on user reviews using both machine learning and lexicon-based approaches to determine item sentiment polarity. The goal is to provide accurate jewelry recommendations by analyzing user ratings and sentiments.
Recommendation algorithms and their variations such as ranking are the most common way for machine learning to find its way into a product where it is not the main focus. In this talk we’ll dig into the subtleties of making recommendation algorithms a seamless and integral part of your UX (goal: it should completely fade into the background. The user should not be aware she’s interacting with any kind of machine learning, it should just feel right, perhaps smart or even a tad like cheating); how to solve the cold start problem (and having little training data in general); and how to effectively collect feedback data. I’ll be drawing from my experiences building Metabase, an open source analytics/BI tool, where we extensively use recommendations and ranking to keep users in a state of flow when exploring data; to help with discoverability; and as a way to gently teach analysis and visualization best practices; all on the way towards building an AI data scientist.
The document provides an overview of design process and factors that affect user experience in interface design. It discusses various principles and heuristics to support usability, including learnability, flexibility, and robustness. The document outlines principles that affect these factors, such as predictability, consistency and dialog initiative. It also discusses guidelines for improving usability through user testing and iterative design. The document emphasizes the importance of usability and provides several heuristics and guidelines to measure and improve usability in interface design.
Introduction to MaxDiff Scaling of Importance - Parametric Marketing SlidesQuestionPro
This document provides an overview of MaxDiff, a technique for evaluating preferences that asks respondents to choose best and worst options from sets. It notes limitations of traditional rating scales like scale bias and lack of constraints. MaxDiff forces trade-offs and provides richer data than ratings. Questions present lists and ask for most/least important. Results can be analyzed simply by counting choices or more advanced techniques can provide respondent-level utilities. The document provides examples and tips for effective MaxDiff surveys.
IRJET- Hybrid Book Recommendation SystemIRJET Journal
This document describes a hybrid book recommendation system that aims to overcome some common issues with recommendation systems like the cold start problem. The system collects demographic information from users during signup to provide more personalized recommendations. It uses both collaborative and content-based filtering approaches. For new users, it recommends books based on their interests. For users without ratings, it considers their purchase history. For users who provide ratings, it uses algorithms like KNN, SVD, RBM and hybrid approaches. The system aims to improve accuracy and provide a more personalized experience for users.
The document discusses the six main steps for building machine learning models: 1) data access and collection, 2) data preparation and exploration, 3) model build and train, 4) model evaluation, 5) model deployment, and 6) model monitoring. It describes each step in detail, including exploring and cleaning the data, choosing a model type, training the model, evaluating model performance on test data, deploying the trained model, and monitoring the model after deployment. The process is iterative, with steps like data preparation and model training often repeated to improve the model.
1) The document discusses writing requirements using a use-case model approach. It emphasizes identifying system actors and their goals, as well as use cases that represent meaningful interactions between actors and the system.
2) Key aspects of developing a use-case model are identifying relevant actors, their roles and responsibilities, then analyzing tasks and goals to derive use cases. Actors can represent both human and non-human entities.
3) Use cases should be written from the perspective of actors to ensure requirements capture intended system functionality and meet user needs. The use-case model provides documentation for various stakeholders throughout the software development lifecycle.
Different Methodologies For Testing Web Application TestingRachel Davis
The document discusses different methodologies for testing web applications, including functionality testing, performance testing, usability testing, compatibility testing, unit testing, load testing, stress testing, and security testing. It provides details on each type of testing, including definitions and the pros and cons of functionality testing specifically. The key methodologies covered are functionality testing, which validates outputs against expected outputs; performance testing, which evaluates a system under pressure; and usability testing, which tests the user-friendliness of an application.
Prompt-Based Techniques for Addressing the Initial Data Scarcity in Personali...IRJET Journal
The document proposes a new method called Prompt Recognition to address the initial data scarcity problem in recommendation systems. It introduces a benchmark for evaluating recommendation approaches under initial data scarcity conditions. Prompt Recognition uses pre-trained language models to generate recommendations based on user and item profiles represented as natural language prompts, without requiring any past user-item interaction data. It represents the recommendation task as predicting sentiment words like "good" and "bad" based on the user-item context generated from profiles. Experimental results on several datasets show the effectiveness of the Prompt Recognition approach.
Personalized recommendation for cold start usersIRJET Journal
The document discusses personalized recommendation methods for cold start users. It describes several recommendation techniques including content-based filtering, collaborative filtering, and hybrid recommendation. It also discusses challenges like cold start problems and data sparsity. Trust-based recommendation systems are described that incorporate social relationships between users. Matrix factorization techniques are discussed for modeling user-item interactions and incorporating additional contextual factors. The use of probabilistic matrix factorization models to address cold start and sparsity problems is also covered.
Investigation and application of Personalizing Recommender Systems based on A...Eswar Publications
To aid in the decision-making process, recommender systems use the available data on the items themselves. Personalized recommender systems subsequently use this input data, and convert it to an output in the form of ordered lists or scores of items in which a user might be interested. These lists or scores are the final result the user will be presented with, and their goal is to assist the user in the decision-making process. The application of recommender systems outlined was just a small introduction to the possibilities of the extension. Recommender
systems became essential in an information- and decision-overloaded world. They changed the way users make decisions, and helped their creators to increase revenue at the same time.
The document proposes a method to recommend users on Q&A sites who are most likely to correctly answer questions. It involves:
1) Classifying questions into tags using logistic regression and SVM models trained on historical data.
2) Calculating a weighted score for each user based on past answer performance for each tag.
3) Recommending top users for tags identified in step 1 as most likely to answer new questions correctly. Experimental results showed this approach worked better for common tags with more training data, while rare tags remained inaccurate to classify. Future work is needed to improve recommendations and user experience.
This document summarizes several research papers on improving recommendation systems using item-based collaborative filtering approaches. It discusses challenges with traditional collaborative filtering like data sparsity and scalability. It then summarizes various item-based recommendation algorithms that analyze item relationships to indirectly compute recommendations. These include item-item similarity techniques like cosine similarity and adjusting for average ratings. The document also reviews literature on combining item categories and interestingness measures to improve accuracy. Overall, it analyzes different item-based collaborative filtering techniques to address challenges and provide high quality, personalized recommendations at scale.
Here's a next step to understanding Recommendation Engine in AI.
Collaborative filtering
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Introduction:
In this never-ending digital world, the essence of a smooth and safe file transfer solution is vital. CrushFTP 10.4.0.29 is a kind of full-featured, robust, and easy-to-use PC software designed for a smooth file transfer process without compromising security. In this review, we will dig in deep regarding the CrushFTP features, functions, and system requirements to have a 360-degree view of its capabilities and possible applications.
Description:
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Abstract:
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2. Tools for
Recommender
Systems
41 - 53
Tools for building systems
quickly
Anatomy of
Recommender
Systems
3 - 19
System components and
terminology
Evaluating
Recommender
Systems
54 - 58
What makes a good
recommender system?
What We
Missed
59 - 63
Other subjects in
recommender systems
Designing
Recommender
Systems
20 - 31
Design considerations and
frameworks
Example
Recommender
Systems
32 - 40
Real-world recommender
systems and their
architectures
Table of
contents
2
4. Recommendation
A recommendation system presents items to users in
a relevant way.
The definition of relevant is product/context-specific.
Recommendation vs Personalization
Personalization
A personalization system presents recommendations
in a way that is relevant to the individual user.
The user expects their experience to change based
on their interactions with the system.
Relevance can still be product/context specific.
7. Users
A user in a recommender system is the party that is
receiving and acting on the recommendations.
Sometimes the user is the context, not an actual
person.
Users vs Items
Items
An item in a recommender system is the passive party
that is being recommended to the users.
The line between these two can be blurry.
8. Example:
Consultant
Matchmaking
(Hypothetical)
*Personalized
Rec Sys #1
Users: Consultants*
Items: Projects
Recommend projects for
the consultant to bid on.
Rec Sys #2
Users: Projects
Items: Consultants
Recommend the right
consultant for the project.
Rec Sys #3
Users: Enterprises*
Items: Consultants
Recommend consultants
for relationship building.
9. Positive
Hearts, stars, likes, listens, watches, follows,
bids, purchases, hires, reads, views, upvotes…
❤
Negative
Bans, skips, angry-face-reacts, 1-star reviews,
rejections, unfollows, returns, downvotes…
Interactions
Explicit vs Implicit
Explicit actions are those that a user expects
or intends to impact their personalized
experience.
Implicit actions are all other interactions
between users and items.
11. Indicator Features
A feature that is unique to every user/item to
allow for direct personalization.
These features allow recommender systems
to learn about every user individually without
being diluted through metadata.
Often one-hot encoded user IDs or just an
identity matrix.
Metadata Features
Age, location, language, tags, labels, word
counts, pre-learned embeddings…
Everything that is known about a user/item
before training can be a feature if properly
structured. Should it be?
Often called “side input” or “shared features.”
User/Item Features
12. User/Item Features
Indicator Features Metadata Features
Encoded
Labels/Tags/et
c.
[n_users x n_user_features]
or
[n_items x n_item_features]
User 1
User 2
User 3
User 4
User 5
User 6
13. Representation
A (typically) low-dimensional vector that
encodes the feature information about the
user or item.
Often called “embedding,” “latent user/item,”
or “latent representation.”
Representation size, which is the dimension of
the latent space, is often referred to as
“components.”
Representation Functions
Representation Function
The process that converts user/item features
in to representations.
Learning happens here.
Common examples:
1. Matrix factorization
2. Linear kernels
3. Deep nets
4. Word2Vec
5. Autoencoders
6. None! (Pass-through)
15. Prediction
A prediction from a recommender system is an
estimate of an item’s relevance to the user.
Predictions can be ranked for relevance.
The predictions are an indirect approximation
of the interactions.
Prediction Functions
Prediction Function
The process that converts user/item
representations in to predictions.
Common examples:
1. Dot product
2. Cosine similarity/distance
3. Euclidean similarity/distance
4. Manhattan similarity/distance*
Some systems use deep nets for prediction,
and this can be an assumption-breaker.
*Actually, Manhattan is rare
16. Prediction Functions
User
Item
Θ
2-Component Latent Representation Space
(2-Dimensional)
Common examples:
1. Dot product = User · Item
2. Cosine similarity = cos(Θ)
3. Euclidean similarity* = ( -1 * δ )
4. Manhattan similarity = ( -1 * |User - Item| )
*There are many methods for expressing euclidean similarity
δ
17. Loss Function
The process that converts predictions and
interactions in to error for learning.
Common examples:
1. Root-mean-square error (RMSE)
2. Kullback-Leibler divergence (KLD)
3. Alternating least squares* (ALS)
4. Bayesian personalized ranking* (BPR)
5. Weighted approximately ranked
pairwise (WARP)
6. Weighted margin-rank batch (WMRB)
*These are both a loss and representation function
Loss and Learning
Learning-to-rank
Some loss functions learn to approximate
the values in the interactions matrix.
Other loss functions learn to uprank positive
interactions and downrank negative
interactions (and/or non-interacted items) for
that user.
This second category of loss functions are
called learning-to-rank.
18. User Features
Item Features
Interactions
User Representation
Item Representation
User Representation
Function
Item Representation
Function
Prediction
Function
Predicted Scores Predicted Ranks
Training Loss
Loss Function
InputData
Output Data
19. Y = Prediction
p = Prediction function
r = Representation function
X = Features
Ɛ = Loss
s = Loss Function
N = Interactions
21. Interactions Features Learning
What are our
interaction values?
We must select interaction values based on
what data is available, how meaningful that
data is, and how it interacts with the rest of the
system.
Considerations
❏ What user behaviors do our interactions
represent?
❏ Explicit vs implicit?
❏ Do we allow for negative interactions?
❏ How dense are our interactions?
❏ Can our recommender handle these
interactions?
How does our system
learn?
We must select representation functions that are
appropriate for our features as well as a
prediction function and loss function that will
learn effectively from this data.
Considerations
❏ What representation functions will best
encode the user/item features?
❏ What prediction function will best estimate
relevance?
❏ What loss function will learn from our data
most effectively?
❏ Do these choices scale?
What are our user/item
features?
We must select user/item features from the
data available, ensure that the data is
meaningful to the recommender system, and
ensure that our use of this data is appropriate.
Considerations
❏ Do we use indicator features?
❏ What useful metadata is available?
❏ Does the metadata require feature
engineering?
❏ Do users expect this metadata to impact
their recommendations?
22. What user behaviors do our
interactions represent?
Interaction values should be an
approximation of the intended effect of the
recommender system on user behavior.
If we want people to purchase, our
interactions should be related to purchases.
If we want people to binge episodes of
shows for longer, our interactions should be
related to the act of binging.
What are our interaction values?
Explicit vs Implicit
When the user gave you this signal, did they
intend/expect it to alter their
recommendations?
Some explicit signals don’t work well as
interactions.
Negative explicit signals should be handled
with simple product logic.
“You might give five stars to Hotel Rwanda and two
stars to Captain America, but you’re much more likely to
watch Captain America.”
-Todd Yellin, Netflix, You May Also Like
23. What are our interaction values?
Explicit vs Implicit
Does the user know we are using this signal for
recommendation?
Does the user care we are using this signal for
recommendation?
Is it ethical for us to use this signal for
recommendation?
24. 1. Positive Positive Positive
2. Positive Positive Positive
3. No-int Negative No-int
4. No-int Negative Negative
5. No-int Negative No-int
6. No-int No-int Negative
7. Negative No-int No-int
8. Negative No-int Negative
9. Negative No-int No-int
Confusing?Do we allow negative
interactions?
Negative interactions can be valuable
statements of what content to avoid.
Negative interactions can be confusing
when learning-to-rank.
Not all loss functions accommodate negative
interactions.
What are our interaction values?
Which ordering is better?
25. Do we use indicator
features?
Indicator features allow for powerful
personalization but are as numerous as our
users/items.
Recommenders with user indicators can not
effectively make recommendations for new
users* (the cold-start problem).
Many users means many indicator features
-- this may not scale.
*Vice-versa is true for new items
What are our user/item features?
What useful metadata is
available?
What user/item metadata do we have that is
relevant?
Metadata that is useful but missing can be
requested from users, crowd-sourced, or
inferred with other ML systems.
26. Does the metadata require
feature engineering?
Pre-processing features can improve
recommender learning.
Some features may be useless/misleading
without feature engineering.
The choice of representation function
impacts the usefulness of feature
engineering.
What are our user/item features?
Do users expect this
metadata to impact their
recommendations?
Is the use of this metadata ethical*?
Users can be surprised when changing
metadata impacts product experience.
*There is a distinction between metadata used in training
and metadata used in evaluation.
27. What representation
functions will best encode
the user/item features?
Linear kernels are effective if all we have are
indicator features or well-engineered
features. (Matrix factorization)
More complex relationships may lead us to
neural nets. How does their architecture
impact the recommender? (Use of the latent
space)
Can the representation be learned without
interaction? (Auto-encoders, word2vec, etc)
How does our system learn?
What prediction function will
best estimate relevance?
Dot-product prediction accounts for
representation relevance and magnitude.
Cosine prediction optimizes for relevance but
has no sense for magnitude.
Euclidean prediction builds a map of items but
also has no sense for magnitude.
Should items be biased, given our choice?
28. What loss function will learn
from our data most
effectively?
Do we want to estimate interactions, or
perform learning-to-rank?
Should the loss function accommodate
negative interactions? (RMSE, KLD…)
Should the loss function be sensitive to
interaction magnitude? (RMSE, B-WMRB…)
Tweaking the loss function can dramatically
change how recommendations feel.
How does our system learn?
Sparse vs Dense vs Sampled
Some implementations of loss functions only
account for user/item pairs with interactions.
These same loss functions can be written to
compare every possible user/item pair. These
predictions and losses are dense, and they can
be expensive.
Some of the most effective and efficient loss
functions learn by comparing pairs with
interactions against sampled pairs.* (WARP,
WMRB)
* There are many methods for sampling candidate pairs
29. Example: WMRB
WMRB approximates positive item rank
against a random sample and upranks
positive items through a hinge loss.
How does our system learn?
x = User
y = Positive item
y’ = Non-positive item
Y = All items
Z = Random sample of non-positive items
p = Prediction function
Hinge
Random
Sampling
30. Example: Balancing WMRB
If we notice an undue popularity bias, we can
balance this by accounting for interaction
magnitudes and popularity.
How does our system learn?
x = User
y = Positive item
X = All users
p = Prediction function
n = Interaction magnitude for pair (user, item)
Balancing
Factor
31. We can think about a recommender system
architecture as a set of top-level decisions.
When designing recommender systems, we
are evaluating the tradeoffs between these
decisions and the relationships between
these choices.
A Framework for Recommender Systems
Interactions ?
User Features ?
User Representation ?
Item Features ?
Item Representation ?
Prediction ?
Learning ?
33. A collaborative filter learns representations
from interactions and uses these to make
personalized recommendations, often
through matrix factorization.
Pure collaborative filters are metadata-naïve.
Example: Collaborative Filter
Interactions *
(Positive only?)
User Features Indicator
User Representation Linear
Item Features Indicator
Item Representation Linear
Prediction *
(Dot-product for MF)
Learning ALS, BPR, SVD, PCA, NMF...
34. A content-based recommender learns the
item features to which a user is affined.
Purely content-based systems do no transfer
learning between users.
This allows easy rec-splanation.
This requires clean item metadata.
Example: Content-based Recommender
Interactions *
User Features Indicator
User Representation Linear
Item Features Metadata
Item Representation None
(n_components = n_item_features)
Prediction Dot-product
Learning *
35. A hybrid recommender system learns
representations for both user and item
metadata and indicators, if available.
This opens a lot of options for us.
Example: Hybrid Recommender System
Interactions *
User Features *
User Representation *
Item Features *
Item Representation *
Prediction *
Learning *
36. We can build a hybrid recommender system
to recommend personalized products based
on past purchases.
Example: Purchase Recommendations
Interactions Purchases
User Features Indicator
User Representation Linear
Item Features Indicator + Metadata
Item Representation *
Prediction Dot-product
Learning *
37. We can use the pre-trained purchase
recommender’s representations to provide
recommendations in a new context.
In this system, the “user” is the context item,
not the person using our product.
Example: “You May Also Like” (YMAL)
Interactions X
User Features Context Item Repr
User Representation None
Item Features All Item Reprs
Item Representation None
Prediction Dot-product, Cosine?
Learning X
38. We can take the output of the YMAL
recommender and re-rank the items based
on the customer’s representation.
This system does not learn. The learning’s
already been done.
Example: Personalized “You May Also Like”
Interactions X
User Features User Reprs
User Representation None
Item Features Similar Item Reprs
Item Representation None
Prediction Dot-product
Learning X
39. Example: Personalized “You May Also Like”
Purchase
Recommender
System
“YMAL”
Recommender
System
“YMAL”
Personalization
System
Step 1:
Learn to personalize
purchasing
recommendations
Step 2:
Use previous learning to
calculate the most similar
items
Step 3:
Personalize the similar
items by re-ranking
OR
Contextualize purchase
recommendations by
limiting the item set
40. Example: YouTube (Covington, Adams, Sargin)
Interactions Watches + Searches
User Features Geography, Age, Gender...
User Representation Deep net
Item Features
Pre-learned embeddings,
language, previous impressions...
Item Representation Deep net
Prediction Deep net
Learning Sampled Cross-Entropy
42. Implicit
Interactions *
User Features Indicator
User Representation Linear
Item Features Indicator
Item Representation Linear
Prediction Dot-product
Learning ALS, BPR
Implicit is a Python collaborative filter toolkit
that uses matrix factorization to learn
representations.
Includes factorization classes for ALS and
BPR.
Made by Ben Frederickson.
MIT License
43. Scikit-Learn
Interactions *
User Features Indicator
User Representation Linear
Item Features Indicator
Item Representation Linear
Prediction Dot-product
Learning SVD, PCA, NMF...
Scikit-learn is a Python machine learning
toolkit with many tools for feature
engineering and machine learning.
The decomposition package contains some
classes that can be used for matrix
factorization recommender systems like SVD,
PCA, NMF...
Maintained by volunteers.
BSD license
44. LightFM
Interactions *
User Features *
User Representation Linear
Item Features *
Item Representation Linear
Prediction Dot-product
Learning Logistic, BPR, WARP
LightFM is a Python hybrid recommender
system that uses matrix factorization to learn
representations.
Made by Lyst - a fashion shopping website.
Apache-2.0 license
45. TensorRec is a Python hybrid recommender
system framework for developing whole
recommender systems quickly.
Representation functions, prediction
functions, and loss functions can be
customized using TensorFlow or Keras.
Made by James Kirk.
Apache-2.0 license
TensorRec
Interactions *
User Features *
User Representation Linear, Deep nets, None...
Item Features *
Item Representation Linear, Deep nets, None...
Prediction
Dot-product, Cosine,
Euclidean...
Learning RMSE, KLD, WMRB...
Hey, that’s me
46. Annoy is a tool for fast similarity search
written in C++ with Python bindings.
Useful for building systems to serve
recommendations from pre-learned
representations.
Made by Spotify.
Apache-2.0 license
ANNOY (Approximate Nearest Neighbors Oh Yeah)
Interactions X
User Features X
User Representation X
Item Features X
Item Representation X
Prediction
Cosine, Euclidean,
Manhattan, Hamming
Learning X
47. Faiss is a tool for fast similarity search
written in C++ with Python bindings.
Useful for building systems to serve
recommendations from pre-learned
representations.
Allows item biases.
Made by Facebook.
BSD license
FAISS (Facebook AI Similarity Search)
Interactions X
User Features X
User Representation X
Item Features X
Item Representation X
Prediction Dot-product, Euclidean
Learning X
48. NMSLib is a tool for fast similarity search
written in C++ with Python bindings.
Useful for building systems to serve
recommendations from pre-learned
representations.
Made by Bilegsaikhan Naidan, Leonid
Boytsov, Yury Malkov, David Novak, Ben
Frederickson.
Apache-2.0 license, with some
MIT and GNU components
NMSLib (Non-Metric Space Library)
Interactions X
User Features X
User Representation X
Item Features X
Item Representation X
Prediction Cosine, Euclidean
Learning X
49. We can build a hybrid recommender system
to recommend personalized news articles
based on past reading.
Requirements:
1. We have to learn the tastes of
individual users.
2. We know users’ home location with
low resolution (country/state).
3. Articles are ephemeral. All items are
cold-start items.
4. We can vectorize article contents and
tagged categories. (politics, sports…)
5. We have to serve production-scale
user traffic.
6. We don’t have to do rec-splanation.
Example: News Article Recommendation
Interactions Clicks, page dwells...
User Features
Indicator +
vectorized locations
User Representation Linear
Item Features
TF-IDF of contents +
vectorized categories
Item Representation Deep net
Prediction Cosine
Learning Balanced WMRB
50. Example: News Article Recommendation
Daily Model Training
Scikit-learn
Feature
Transformation
TensorRec
Recommender
System
Annoy
Ranking
Step 1:
Vectorize historical
article contents and
metadata
Step 2:
Use vectorized article
features to learn user
representations and train
a deep net for article
representation
Step 3:
Build Annoy indices
53. Example: MovieLens with TensorRec
Interactions Movie ratings
User Features Indicator
User Representation Linear
Item Features Indicator + Movie Tags
Item Representation Linear
Prediction Dot-product
Learning Balanced WMRB
55. Offline Evaluation
Many metrics are available for offline
evaluation to comparing predictions and
known interactions.
Most measure novelty, diversity, and
coverage.
Precision@K, Recall@K, NDCG@K…
Precision@K: “What percentage of the top K
items were positively interacted?”
Recall@K: “What percentage of users’
positively interacted items were in the top K
results?”
What makes a good recommender system?
Offline Pitfalls
Many offline metrics don’t represent fairness
of performance between users or items.
These metrics can be useful for
hyperparameter optimization, but often fail to
evaluate the “feel” of recommendations.
It is hard to use offline metrics to state that
one recommender system is better than
another.
56. Example: Offline Pitfalls
Three recommendation results for two users.
User 1 has 5 positive interactions.
User 2 has 2 positive interactions.
The third recommendation system is the
most broadly effective, and probably the
“best.” Precision fails to identify that, but
recall does.
You can concoct similar pitfalls for recall or
NDCG.
What makes a good recommender system?
1 2 1 2 1 2
T T T T T
T T T T
T T T
T T
T
P@5: 0.5 P@5: 0.5 P@5: 0.5
R@5: 0.65 R@5: 0.5 R@5: 0.8
57. Online Evaluation
When rolling-out a new recommender
system, the truest test is an A/B test with an
existing system.
The most effective feedback comes from
user interviewing and monitoring the user
behaviors the system is intended to drive.
If there is no existing system, do phased
roll-outs with quant/qual feedback.*
User interviewing is the only way to evaluate
the “feel” of recommendations.
*Fellow employees make great, but biased, guinea pigs
What makes a good recommender system?
Feel?
“I already own a crib, why would I need
another?”
Missing item filtering based on metadata?
“These songs are excellent, but I already
know these bands.”
Maybe we should target discovery?
“I’ve watched Captain America twenty
times, but that doesn’t mean I only want to
watch Marvel movies. What about the
sitcoms I watch?”
Maybe we’re oversimplifying the user’s
representation?
58. All Algorithms Are Biased
There are biases innate in the data we use,
the way users interact with our products, and
the way our algorithms learn.
Controlling for this is not as simple as setting
biased=False.
When designing these systems, we have a
responsibility to, at the least, understand the
biases in our products.
You wouldn’t ship a product without tests.
You shouldn’t ship a RecSys without
examining bias.
Algorithmic Bias and Fairness
Understanding Fairness
There are many of definitions of fairness.
Some cross-section recommender
performance by user and item metadata.
C-fairness
Is recommendation recall significantly lower
for customers in Massachusetts?
P-fairness
Are movies with female leads recommended
less often than in the natural distribution of
movie watching?
Missing metadata? Crowdsource it, but be
careful with sensitive metadata.
60. 1
2
3
4
5
6
What We Missed
Sequence-based models
In what order do our users interact with
our items?
Mixture-of-tastes models
Is one representation per user enough
for users with diverse tastes?
Rec-splanation
How do system design choices impact
interpretability?
Attention models
Can we learn more nuance to user
representation that just a vector?
Graphical models
Can we map relationships between
users, items, and their attributes?
Cold-start problems
How do we make recommendations for
brand-new users?
61. Wait, is it “recommender systems”
or “recommendation systems?”
62. Wait, is it “recommender systems”
or “recommendation systems?”
¯_(ツ)_/¯