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Rating prediction by exploring user’s preference and sentiment

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Abstract

With the development of e-commerce, shopping on-line is becoming more and more popular. The explosion of reviews have led to a serious problem, information overloading. How to mine user interest from these reviews and understand users’ preference is crucial for us. Traditional recommender systems mainly use structured data to mine user interest preference, such as product category, user’s tag, and the other social factors. In this paper, we firstly use LDA+Word2vec model to mine user interest. Then, we propose a social user sentimental measurement approach. At last, three factors, including user topic, user sentiment and interpersonal influence, are fused into a recommender system (RS) based on probabilistic matrix factorization. We conduct a series of experiments on Yelp dataset, and experimental results show the proposed approach outperforms the existing approaches.

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Acknowledgements

This work is partly supported by the NSFC under 61572083, the China Fundamental Research Funds for the Central Universities under Grant 310824153508 and 310824173401 (Chang’an University).

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Authors and Affiliations

  1. School of Information Engineering, Chang’an University, Xi’an, China

    Xiang Ma

  2. The Ministry of Education Key Laboratory for Intelligent Networks and Network Security and with Xi’an Jiaotong University, Xi’an, 710049, China

    Xiaojiang Lei, Guoshuai Zhao & Xueming Qian

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  1. Xiang Ma
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  2. Xiaojiang Lei
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  4. Xueming Qian
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Correspondence to Xueming Qian.

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Ma, X., Lei, X., Zhao, G. et al. Rating prediction by exploring user’s preference and sentiment. Multimed Tools Appl 77, 6425–6444 (2018). https://doi.org/10.1007/s11042-017-4550-z

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