Extracting Deep Semantic Information for Intelligent Recommendation
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.
KeywordsRecommender system Deep learning Text mining
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).
- 3.Mcauley, J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of 7th ACM Conference on Recommender Systems, pp. 165–172 (2013)Google Scholar
- 4.Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 448–456. ACM (2011)Google Scholar
- 5.Ling, G., Lyu, M.R., King, I., et al.: Ratings meet reviews, a combined approach to recommend. In: Proceedings of 8th ACM Conference on Recommender Systems, pp. 105–112 (2014)Google Scholar
- 6.Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)
- 8.Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: International Conference on Machine Learning, pp. 46–54 (1998)Google Scholar
- 10.Salakhutdinov, R., Mnih, A., Hinton, G.E., et al.: Restricted Boltzmann machines for collaborative filtering. In: Proceedings of 24th International Conference on Machine Learning, pp. 791–798 (2007)Google Scholar
- 11.Gao, J., Pantel, P., Gamon, M., et al.: Modeling interestingness with deep neural networks. In: Conference on Empirical Methods in Natural Language Processing, pp. 2–13 (2014)Google Scholar
- 12.Cheng, H.T., Koc, L., Harmsen, J., et al.: Wide & deep learning for recommender systems. In: Proceedings of 1st Workshop on Deep Learning for Recommender Systems, pp. 7–10 (2016)Google Scholar
- 13.Wang, H., Wang, N., Yeung, D., et al.: Collaborative deep learning for recommender systems. In: Proceedings of 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1235–1244 (2015)Google Scholar
- 14.Almahairi, A., Kastner, K., Cho, K., et al.: Learning distributed representations from reviews for collaborative filtering. In: Proceedings of 9th ACM Conference on Recommender Systems, pp. 147–154 (2015)Google Scholar
- 15.Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)