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2018 | OriginalPaper | Chapter

TSAUB: A Temporal-Sentiment-Aware User Behavior Model for Personalized Recommendation

Authors : Qinyong Wang, Hongzhi Yin, Hao Wang, Zi Huang

Published in: Databases Theory and Applications

Publisher: Springer International Publishing

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Abstract

Personalized recommender system has become an essential means to help people discover attractive and interesting items. We find that to buy an item, a user is influenced not only by her intrinsic interests and temporal contexts, but also by the crowd sentiment to this item. Users tend to refuse to accept the recommended items whose most reviews are negative. In light of this, we propose a temporal-sentiment-aware user behavior model (TSAUB) to learn personal interests, temporal contexts (i.e., temporal preferences of the public) and crowd sentiment from user review data. Based on the learnt knowledge from TSAUB, we design a temporal-sentiment-aware recommender system. To improve the training efficiency of TSAUB, we develop a distributed learning algorithm for model parameter estimation using the Spark framework. Extensive experiments have been performed on four Amazon datasets, and the results show that our recommender system significantly outperforms the state-of-the-arts by making more effective and efficient recommendations.

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previous chapter An Efficient Framework for the Analysis of Big Brain Signals Data
next chapter Finding Maximal Stable Cores in Social Networks
Footnotes
1
We use “behaviors” to refer to a broad range of user actions such as purchases, clicks and writing reviews.
 
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Metadata
Title
TSAUB: A Temporal-Sentiment-Aware User Behavior Model for Personalized Recommendation
Authors
Qinyong Wang
Hongzhi Yin
Hao Wang
Zi Huang
Copyright Year
2018
Book
Databases Theory and Applications

Print ISBN: 978-3-319-92012-2

Electronic ISBN: 978-3-319-92013-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-92013-9_17

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