Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41297

Effective Matrix Factorization for Online Rating Prediction

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Item Summary

Title: Effective Matrix Factorization for Online Rating Prediction
Authors: Zhou, Bowen
Wong, Raymond
Keywords: Collaborative filtering
matrix factorization
recommendation
Issue Date: 04 Jan 2017
Abstract: Recommender systems have been widely utilized by online merchants and online advertisers to promote their products in order to improve profits. By evaluating customer interests based on their purchase history and relating it to commodities for sale these retailers could excavate out products which are most likely to be chosen by a specific customer. In this case, online ratings given by customers are of great interest as they could reflect different levels of customers’ interest on different products. Collaborative Filtering (CF) approach is chosen by a large amount of web-based retailers for their recommender systems because CF operates on interactions between customers and products. In this paper, a major approach of CF, Matrix Factorization, is modified to give more accurate recommendations by predicting online ratings.
Pages/Duration: 10 pages
URI/DOI: http://hdl.handle.net/10125/41297
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.144
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Deep Learning, Ubiquitous and Toy Computing Minitrack



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