Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation
ACM Transactions on Internet Technology (TOIT)
Intent-oriented diversity in recommender systems
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Adaptive diversification of recommendation results via latent factor portfolio
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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In this paper, we propose a set-oriented personalized ranking model for diversified top-N recommendation. Users may have various individual ranges of interests. For personalized top-N recommendation task, the combination of relevance and diversity in recommendation results would be desirable. For this purpose, we integrate the concept of diversity into traditional matrix factorization model to construct a set-oriented collaborative filtering model. By optimizing this model with a set-oriented pairwise ranking method, we directly achieve personalized top-N recommendation results which are both relevant and diversified. We also utilize category information explicitly for learning personalized diversity. Experimental results show that our model outperforms traditional models in terms of personalized diversity and maintains good performance on relevance prediction.