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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Notes
- 1.
We use “behaviors” to refer to a broad range of user actions such as purchases, clicks and writing reviews.
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Acknolwledgement
This work was supported by ARC Discovery Early Career Researcher Award (Grant No. DE160100308), ARC Discovery Project (Grant No. DP170103954) and New Staff Research Grant of The University of Queensland (Grant No.613134).
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Wang, Q., Yin, H., Wang, H., Huang, Z. (2018). TSAUB: A Temporal-Sentiment-Aware User Behavior Model for Personalized Recommendation. In: Wang, J., Cong, G., Chen, J., Qi, J. (eds) Databases Theory and Applications. ADC 2018. Lecture Notes in Computer Science(), vol 10837. Springer, Cham. https://doi.org/10.1007/978-3-319-92013-9_17
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