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
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic relevance ranking for collaborative filtering
Information Retrieval
CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Mean-Variance Analysis: A New Document Ranking Theory in Information Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Novel Item Recommendation by User Profile Partitioning
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Exploiting query reformulations for web search result diversification
Proceedings of the 19th international conference on World wide web
Temporal diversity in recommender systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
List-wise learning to rank with matrix factorization for collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Text retrieval methods for item ranking in collaborative filtering
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Intent-oriented diversity in recommender systems
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Precision-oriented evaluation of recommender systems: an algorithmic comparison
Proceedings of the fifth ACM conference on Recommender systems
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
IEEE Transactions on Knowledge and Data Engineering
Explicit relevance models in intent-oriented information retrieval diversification
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
On the role of novelty for search result diversification
Information Retrieval
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Diversity as a quality dimension for Recommender Systems has been receiving increasing attention in the last few years. This has been paralleled by an intense strand of research on diversity in search tasks, and in fact converging views on diversity theories and techniques from Information Retrieval and Recommender Systems have been put forward in recent work. In this paper we research diversity not only as a target property for a recommender system, but as an element in the input data, within and between user behaviors, that a recommender system can leverage to enhance the quality of its output in terms of the balance between accuracy and diversity. We propose an adaptation of search result diversification methods to recommender systems based on query reformulation: we identify the diversity within user profiles and generate partial recommendations based on homogeneous subsets of user preferences (sub-profiles), which we combine later to produce a final recommendation. We report experiments on movie and music recommendation datasets showing that our approach improves indeed the quality of state-of-the-art recommenders, and is competitive against diversification methods that use explicitly item categories as the units for diversification. Our approach shows further advantages in cases where the high cardinality of the explicit category spaces can pose a problem in terms of computational cost.