User Similarity Adjustment for Improved Recommendations
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- User Similarity Adjustment for Improved Recommendations
Recommendations
Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings
Advances in Information RetrievalAbstractNowadays, item recommendation is an increasing concern for many companies. Users tend to be more reactive than proactive for solving information needs. Recommendation accuracy became the most studied aspect of the quality of the suggestions. ...
Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering
RecSys '09: Proceedings of the third ACM conference on Recommender systemsAn approach to user-based collaborative filtering is proposed that refines prediction of item ratings that is based on global user similarity by incorporating information derived from a more detailed user comparison made on the basis of Rated Item Pools ...
A knowledge reuse framework for improving novelty and diversity in recommendations
CODS '15: Proceedings of the 2nd ACM IKDD Conference on Data SciencesRecommender system (RS) is an important instrument in e-commerce, which provides personalized recommendations to individual user. Classical algorithms in recommender system mainly emphasize on recommendation accuracy in order to match individual user's ...
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Berlin, Heidelberg
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