An Efficient Recommender System Using Locality Sensitive Hashing

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dc.contributor.authorZhang, Kunpeng
dc.contributor.authorFan, Shaokun
dc.contributor.authorWang, Harry Jiannan
dc.date.accessioned2017-12-28T00:40:40Z
dc.date.available2017-12-28T00:40:40Z
dc.date.issued2018-01-03
dc.description.abstractRecommender 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.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2018.098
dc.identifier.isbn978-0-9981331-1-9
dc.identifier.urihttp://hdl.handle.net/10125/49985
dc.language.isoeng
dc.relation.ispartofProceedings of the 51st Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBig Data and Analytics: Pathways to Maturity
dc.subjectRecommender System, Locality Sensitive Hashing, Collaborative filtering, minHash, simHash
dc.titleAn Efficient Recommender System Using Locality Sensitive Hashing
dc.typeConference Paper
dc.type.dcmiText

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