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Machine Learning for Q&A Sites: State of the Art and Research Directions

Author:

Xavier AmatriainAuthors Info & Claims

WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web

April 2016

Page 665

https://doi.org/10.1145/2872518.2890572

Published: 11 April 2016 Publication History

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Abstract

Q&A sites like Quora aim at growing the world's knowledge. In order to do this, they need to get the right questions to the right people to answer them, but also the existing answers to people who are interested in them. In order to accomplish this, they need to build a complex ecosystem where issues such as content quality, engagement, demand, interests, or reputation are taken into account. It is not possible to build a system like this unless most of the process are highly automated and scalable. The good news is that using high-quality data you can build machine learning solutions that can help address all of the previous requirements.

In this talk I will describe some interesting uses of machine learning for Q&A that range from different recommendation approaches such as personalized ranking to classifiers built to detect duplicate questions or spam. I will describe some of the modeling and feature engineering approaches that go into building these systems. I will also share some of the challenges faced when building such a large-scale knowledge base of human-generated knowledge. Finally, I will describe some of the unresolved research challenges in the Q&A Space.

I will use my experience at Quora as the main driving example. Quora is a Q&A site that despite having over 80 million unique visitors a month, it is known for keeping a high-quality of answers and content in general.

Index Terms

Machine Learning for Q&A Sites: State of the Art and Research Directions
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Question answering

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Published In

WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web

April 2016

1094 pages

ISBN:9781450341448

General Chairs:
Jacqueline Bourdeau
Tele-university (TELUQ), Montreal, QC, Canada
,
Jim A. Hendler
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Roger Nkambou Nkambou
Université du Québec à Montréal, Montreal, QC, Canada
,
Program Chairs:
Ian Horrocks
University of Oxford, UK
,
Ben Y. Zhao
University of California at Santa Barbara, CA, USA

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

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Published: 11 April 2016

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WWW '16

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IW3C2

WWW '16: 25th International World Wide Web Conference

April 11 - 15, 2016

Québec, Montréal, Canada

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WWW '16 Companion Paper Acceptance Rate 115 of 727 submissions, 16%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Recommendations

Predictors of answer quality in online Q&A sites

Learning to be a better Q'er in social Q&A sites: social norms and information artifacts

Why users of yahoo!: answers do not answer questions

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Abstract

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Recommendations

Predictors of answer quality in online Q&A sites

Learning to be a better Q'er in social Q&A sites: social norms and information artifacts

Why users of yahoo!: answers do not answer questions

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