The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Probabilistic question recommendation for question answering communities
Proceedings of the 18th international conference on World wide web
Recommendation Diversification Using Explanations
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Routing Questions to the Right Users in Online Communities
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Efficient methods for topic model inference on streaming document collections
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic models of ranking novel documents for faceted topic retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Self-presentation and the value of information in Q&A websites
Journal of the American Society for Information Science and Technology
Towards recency ranking in web search
Proceedings of the third ACM international conference on Web search and data mining
Time is of the essence: improving recency ranking using Twitter data
Proceedings of the 19th international conference on World wide web
The anatomy of a large-scale social search engine
Proceedings of the 19th international conference on World wide web
Automatic evaluation of topic coherence
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
ACM SIGMOD Record
Routing questions to appropriate answerers in community question answering services
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Predicting best answerers for new questions in community question answering
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Scalable distributed inference of dynamic user interests for behavioral targeting
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
I want to answer; who has a question?: Yahoo! answers recommender system
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Diversification and refinement in collaborative filtering recommender
Proceedings of the 20th ACM international conference on Information and knowledge management
Question routing in community question answering: putting category in its place
Proceedings of the 20th ACM international conference on Information and knowledge management
Optimizing semantic coherence in topic models
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning from the past: answering new questions with past answers
Proceedings of the 21st international conference on World Wide Web
A classification-based approach to question routing in community question answering
Proceedings of the 21st international conference companion on World Wide Web
Finding expert users in community question answering
Proceedings of the 21st international conference companion on World Wide Web
Churn prediction in new users of Yahoo! answers
Proceedings of the 21st international conference companion on World Wide Web
Diversity by proportionality: an election-based approach to search result diversification
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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What makes a good question recommendation system for community question-answering sites? First, to maintain the health of the ecosystem, it needs to be designed around answerers, rather than exclusively for askers. Next, it needs to scale to many questions and users, and be fast enough to route a newly-posted question to potential answerers within the few minutes before the asker's patience runs out. It also needs to show each answerer questions that are relevant to his or her interests. We have designed and built such a system for Yahoo! Answers, but realized, when testing it with live users, that it was not enough. We found that those drawing-board requirements fail to capture user's interests. The feature that they really missed was diversity. In other words, showing them just the main topics they had previously expressed interest in was simply too dull. Adding the spice of topics slightly outside the core of their past activities significantly improved engagement. We conducted a large-scale online experiment in production in Yahoo! Answers that showed that recommendations driven by relevance alone perform worse than a control group without question recommendations, which is the current behavior. However, an algorithm promoting both diversity and freshness improved the number of answers by 17%, daily session length by 10%, and had a significant positive impact on peripheral activities such as voting.