Research Area: Machine Learning
Collaborative filtering-based recommender systems are regarded as an important tool to predict the items that users will appreciate based on the historical usage of users. However, traditional recommendation solutions often pay more attentions to the accuracy of the recommended items while neglect the diversity of the final recommended list, which may produce partial redundant items in the recommended list and as a result, decrease the satisfaction degree of users. Moreover, historical usage data for recommendation decision-makingoften update frequently, which may lead to low recommendation efficiency as well as scalability especially in the big data environment. Considering these drawbacks, a novel method called DivRec_LSH is proposed in this paper to achieve diversified and efficient recommendations, which is based on the historical usage records and the Locality-Sensitive Hashing (LSH) technique. Finally, we compare our method with existing methods on the MovieLens dataset. Experiment results indicate that our proposal is feasible in addressing the triple dilemmas of recommender systems simultaneously, i.e., high efficiency, accuracy and diversity.
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Author(s) Name: Lina Wang,Xuyun Zhang,Ruili Wang,Huaizhen Kou,Lianyong Qi
Journal name: Knowledge-Based Systems
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Publisher name: Elsevier
DOI: 10.1016/j.knosys.2020.106196
Volume Information: Volume 204, 27 September 2020, 106196
