A time-based approach to effective recommender systems using implicit feedback

Lee, Tong Queue

doi:10.1016/j.eswa.2007.06.031

A time-based approach to effective recommender systems using implicit feedback

2008-05-01

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Lee, Tong Queue

DOI：10.1016/j.eswa.2007.06.031

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Recommender systems provide personalized recommendations on products or services to customers. Collaborative filtering is a widely used method of providing recommendations using explicit ratings on items from users. In some e-commerce environments, however, it is difficult to collect explicit feedback data; only implicit feedback is available. In this paper, we present a method of building an effective collaborative filtering-based recommender system for an e-commerce environment without explicit feedback data. Our method constructs pseudo rating data from the implicit feedback data. When building the pseudo rating matrix, we incorporate temporal information such as the user's purchase time and the item's launch time in order to increase recommendation accuracy. Based on this method, we built both user-based and item-based collaborative filtering-based recommender systems for character images (wallpaper) in a mobile e-commerce environment and conducted a variety of experiments. Empirical results show our time-incorporated recommender system is significantly more accurate than a pure collaborative filtering system. (c) 2007 Elsevier Ltd. All rights reserved.

Keyword:

e-commerce

recommender system

collaborative filtering

implicit feedback

temporal information

mobile environment

A time-based approach to effective recommender systems using implicit feedback

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