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An Efficient Recommender System Using Locality Sensitive Hashing

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Title:An Efficient Recommender System Using Locality Sensitive Hashing
Authors:Zhang, Kunpeng
Fan, Shaokun
Wang, Harry Jiannan
Keywords:Big Data and Analytics: Pathways to Maturity
Recommender System, Locality Sensitive Hashing, Collaborative filtering, minHash, simHash
Date Issued:03 Jan 2018
Abstract:Recommender systems are widely used for personalized recommendation in many business applications such as online shopping websites and social network platforms. However, with the tremendous growth of recommendation space (e.g., number of users, products, etc.), traditional systems suffer from time and space complexity issues and cannot make real-time recommendations when dealing with large-scale data. In this paper, we propose an efficient recommender system by incorporating the locality sensitive hashing (LSH) strategy. We show that LSH can approximately preserve similarities of data while significantly reducing data dimensions. We conduct experiments on synthetic and real-world datasets of various sizes and data types. The experiment results show that the proposed LSH-based system generally outperforms traditional item-based collaborative filtering in most cases in terms of statistical accuracy, decision support accuracy, and efficiency. This paper contributes to the fields of recommender systems and big data analytics by proposing a novel recommendation approach that can handle large-scale data efficiently.
Pages/Duration:10 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Big Data and Analytics: Pathways to Maturity

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