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2018 | OriginalPaper | Chapter

TUP-RS: Temporal User Profile Based Recommender System

Authors : Wanling Zeng, Yang Du, Dingqian Zhang, Zhili Ye, Zhumei Dou

Published in: Artificial Intelligence and Soft Computing

Publisher: Springer International Publishing

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Abstract

As e-commerce continues to emerge in recent years, online stores compete intensely to improve the quality of recommender systems. However, most existing recommender systems failed to consider both long-term and short-term preferences of users based on purchase behavior patterns, ignoring the fact that requirements of users are dynamic. To this end, we present TUP-RS (Temporal User Profile based Recommender System) in this paper. Specifically, the contributions of this paper are two folds: (i) the long-term and short-term preferences from the topic model are combined to construct the temporal user profiles; (ii) the co-training method which shares the parameters in the same feature space is employed to increase the accuracy. We study a subset of data from Amazon and demonstrate that TUP-RS outperforms state-of-the-art methods. Moreover, our recommendation lists are time-sensitive.

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Title: TUP-RS: Temporal User Profile Based Recommender System
Authors: Wanling Zeng
Yang Du
Dingqian Zhang
Zhili Ye
Zhumei Dou
Publisher: Springer International Publishing
Book: Artificial Intelligence and Soft Computing
Print ISBN: 978-3-319-91261-5

Electronic ISBN: 978-3-319-91262-2

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-91262-2_42

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