Abstract
In recent years, there have been many works focusing on combing ratings and reviews to improve the performance of recommender system. Comparing with the rating based algorithms, these methods can be used to alleviate the data sparsity problem in a certain extent. However, they lack the ability to extract the deep semantic information from plaintext reviews. In addition, they do not take the consistence of the latent semantic space of user profiles and item representations into account. To address these problems, we propose a novel method named as Deep Semantic Hybrid Recommendation Method (DSHRM). We utilize deep learning technologies to extract user profiles and item representations from reviews and make sure both of them are in a consistent latent semantic space. We combine ratings and reviews to generate better recommendations. Extensive experiments on real-world datasets show that our method significantly outperforms other six state-of-the-art methods, including LFM, SVD++, CTR, RMR, BoWLF and LMLF methods.
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Acknowledgements
This research is supported by National Natural Science Foundation of China (Grant No. 61375054), Natural Science Foundation of Guangdong Province (Grant No. 2014A030313745), Basic Scientific Research Program of Shenzhen City (Grant No. JCYJ20160331184440545), and Cross fund of Graduate School at Shenzhen, Tsinghua University (Grant No. JC20140001).
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Chen, W., Zheng, HT., Mao, XX. (2017). Extracting Deep Semantic Information for Intelligent Recommendation. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_14
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DOI: https://doi.org/10.1007/978-3-319-70139-4_14
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