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
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
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
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
It takes variety to make a world: diversification in recommender systems
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Local search for balanced submodular clusterings
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Trust based recommender system for the semantic web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Beyond accuracy: evaluating recommender systems by coverage and serendipity
Proceedings of the fourth ACM conference on Recommender systems
A class of submodular functions for document summarization
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Diversification and refinement in collaborative filtering recommender
Proceedings of the 20th ACM international conference on Information and knowledge management
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We study the problem of diverse promoting recommendation task: selecting a subset of diverse items that can better predict a given user's preference. Recommendation techniques primarily based on user or item similarity can suffer from the risk that users cannot get expected information from the over-specified recommendation lists. In this paper, we propose an entropy regularizer to capture the notion of diversity. The entropy regularizer has good properties in that it satisfies monotonicity and submodularity, such that when we combine it with a modular rating set function, we get submodular objective function, which can be maximized approximately by efficient greedy algorithm, with provable constant factor guarantee of optimality. We apply our approach on the top-K prediction problem and evaluate its performance on Movie-Lens data set, which is a standard database containing movie rating data collected from a popular online movie recommender system. We compare our model with the state-of-the-art recommendation algorithms. Our experiments show that entropy regularizer effectively captures diversity and hence improves the performance of recommendation task.