The effect of context-aware recommendations on customer purchasing behavior and trust
Proceedings of the fifth ACM conference on Recommender systems
Rank and relevance in novelty and diversity metrics for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Workshop on novelty and diversity in recommender systems - DiveRS 2011
Proceedings of the fifth ACM conference on Recommender systems
A personalized recommendation system on scholarly publications
Proceedings of the 20th ACM international conference on Information and knowledge management
Topic analysis for online reviews with an author-experience-object-topic model
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
New approaches to diversity and novelty in recommender systems
FDIA'11 Proceedings of the Fourth BCS-IRSG conference on Future Directions in Information Access
Structural context-aware cross media recommendation
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
A group recommender for movies based on content similarity and popularity
Information Processing and Management: an International Journal
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Serendipitous Personalized Ranking for Top-N Recommendation
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
A new collaborative filtering approach for increasing the aggregate diversity of recommender systems
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Real-time recommendation of diverse related articles
Proceedings of the 22nd international conference on World Wide Web
Exploiting the diversity of user preferences for recommendation
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
A framework for learning and analyzing hybrid recommenders based on heterogeneous semantic data
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Spatial search for K diverse-near neighbors
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Proceedings of the 7th ACM conference on Recommender systems
Proceedings of the 7th ACM international conference on Web search and data mining
Who likes it more?: mining worth-recommending items from long tails by modeling relative preference
Proceedings of the 7th ACM international conference on Web search and data mining
Multi-objective mobile app recommendation: A system-level collaboration approach
Computers and Electrical Engineering
QA document recommendations for communities of question-answering websites
Knowledge-Based Systems
Comparing context-aware recommender systems in terms of accuracy and diversity
User Modeling and User-Adapted Interaction
Clustering-based diversity improvement in top-N recommendation
Journal of Intelligent Information Systems
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Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce and explore a number of item ranking techniques that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating data sets and different rating prediction algorithms.