Effect of Different Implicit Social Networks on Recommending Research Papers
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- Effect of Different Implicit Social Networks on Recommending Research Papers
Recommendations
On the recommending of citations for research papers
CSCW '02: Proceedings of the 2002 ACM conference on Computer supported cooperative workCollaborative filtering has proven to be valuable for recommending items in many different domains. In this paper, we explore the use of collaborative filtering to recommend research papers, using the citation web between papers to create the ratings ...
A novel learning-to-rank based hybrid method for book recommendation
WI '17: Proceedings of the International Conference on Web IntelligenceRecommendation system is able to recommend items that are likely to be preferred by the user. Hybrid recommender systems combine the advantages of the collaborative filtering and content-based filtering for improved recommendation. Hybrid recommendation ...
Recommending new movies: even a few ratings are more valuable than metadata
RecSys '09: Proceedings of the third ACM conference on Recommender systemsThe Netflix Prize (NP) competition gave much attention to collaborative filtering (CF) approaches. Matrix factorization (MF) based CF approaches assign low dimensional feature vectors to users and items. We link CF and content-based filtering (CBF) by ...
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- General Chairs:
- Julita Vassileva,
- James Blustein,
- Program Chairs:
- Lora Aroyo,
- Sidney D'Mello
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Association for Computing Machinery
New York, NY, United States
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- Short-paper
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- Institute of Public Administration, Saudi Arabia
- Natural Sciences and Engineering Research Council of Canada
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