JACIII Vol.23 No.5 pp. 864-873
doi: 10.20965/jaciii.2019.p0864


Study on Collaborative Filtering Recommendation Model Fusing User Reviews

Heyong Wang, Ming Hong, and Jinjiong Lan

Department of E-Commerce, School of Economics and Commerce, South China University of Technology
B10 South China University of Technology, Panyu District, Guangzhou, Guangdong 510006, China

August 10, 2018
April 1, 2019
September 20, 2019
collaborative filtering, user reviews, topic model, similarity fusion

The traditional collaborative filtering model suffers from high-dimensional sparse user rating information and ignores user preference information contained in user reviews. To address the problem, this paper proposes a new collaborative filtering model UL_SAM (UBCF_LDA_SIMILAR_ADD_MEAN) which integrates topic model with user-based collaborative filtering model. UL_SAM extracts user preference information from user reviews through topic model and then fuses user preference information with user rating information by similarity fusion method to create fusion information. UL_SAM creates collaborative filtering recommendations according to fusion information. It is the advantage of UL_SAM on improving recommendation effectiveness that UL_SAM enriches information for collaborative recommendation by integrating user preference with user rating information. Experimental results of two public datasets demonstrate significant improvement on recommendation effectiveness in our model.

Cite this article as:
H. Wang, M. Hong, and J. Lan, “Study on Collaborative Filtering Recommendation Model Fusing User Reviews,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.5, pp. 864-873, 2019.
Data files:
  1. [1] J. G. Liu, T. Zhou, and B. H. Wang, “Advances in personalized recommendation system,” Progress in Natural Science, Vol.19, No.1, pp. 1-15, 2009 (in Chinese).
  2. [2] I. Obadić, G. Madjarov, I. Dimitrovski, and D. Gjorgjevikj, “Addressing Item-Cold Start Problem in Recommendation Systems Using Model Based Approach and Deep Learning,” Int. Conf. on ICT Innovations, pp. 176-185, 2017.
  3. [3] L. Zheng, V. Noroozi, and P. S. Yu, “Joint Deep Modeling of Users and Items Using Reviews for Recommendation,” Proc. of 10th ACM Int. Conf. on Web Search and Data Mining, pp. 425-434, 2017.
  4. [4] S. Deng, L. Huang, G. Xu, X. Wu, and Z. Wu, “On deep learning for trust-aware recommendations in social networks,” IEEE Trans. on Neural Networks and Learning Systems, Vol.28, No.5, pp. 1164-1177, 2016.
  5. [5] X. H. Ling, X. Wu, X. D. Li, and B. P. Yan, “Comparison Study of Internet Recommendation System,” J. of Software, Vol.20, No.2, pp. 350-362, 2009.
  6. [6] A. L. Deng, Y. Y. Zhu, and B. L. Shi, “A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction,” J. of Software, Vol.9, No.14, pp. 1621-1628, 2003.
  7. [7] J. Yuan, W. Shalaby, M. Korayem, D. Lin, K. AlJadda, and J. Luo, “Solving cold-start problem in large-scale recommendation engines: A deep learning approach,” IEEE Int. Conf. on Big Data, pp. 1901-1910, 2016.
  8. [8] S. Cao, N. Yang, and Z, Liu, “Online Short Video Recommendation Based on Stacked Denoising Auto-Encoder,” J. of Residuals Science & Technology, Vol.13, No.8, pp. 1-6, 2016.
  9. [9] X. Dong, L. Yu, Z. Wu, Y. Sun, L. Yuan, and F. Zhang, “A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems,” Proc. of the 31st AAAI Conf. on Artificial Intelligence (AAAI-17), pp. 1309-1315, 2017.
  10. [10] M. Polato and F. Aiolli, “Boolean kernels for collaborative filtering in top-N item recommendation,” Neurocomputing, Vol. 286, pp. 214-225, 2018.
  11. [11] H. Li, K. Li, J. An, and K. Li, “MSGD: A Novel Matrix Factorization Approach for Large-scale Collaborative Filtering Recommender Systems on GPUs,” IEEE Trans. on Parallel and Distributed Systems, Vol.29, No.7, pp. 1530-1544, 2018.
  12. [12] R. van den Berg, T. N. Kipf, and M. Welling, “Graph Convolutional Matrix Completion,” arXiv: 1706.02263, pp. 1-10, 2017.
  13. [13] S. Deng, L. Huang, G. Xu, X. Wu, and Z. Wu, “On deep learning for trust-aware recommendations in social networks,” IEEE Trans. on Neural Networks and Learning Systems, Vol.28, No.5, pp. 1164-1177, 2016.
  14. [14] T. Gao, X. Li, Y. Chai, and Y. Tang, “Deep learning with consumer preferences for recommender system,” 2016 IEEE Int. Conf. on Information and Automation (ICIA), pp. 1556-1561, 2016.
  15. [15] G. N. Hu, X. Y. Dai, F. Y. Qiu, R. Xia, T. Li, S.-J. Huang, and J.-J. Chen, “Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback,” ACM Trans. on Knowledge Discovery from Data, Vol.12, No.2, Article No.23, 2018.
  16. [16] N. Guo, B. Wang, Y. Hou, and P. Chang, “Collaborative Filtering Recommendation Algorithm Based on Characteristics of Social Network,” J. of Frontiers of Computer Science and Technology, Vol.12, No.2, pp. 208-217, 2018.
  17. [17] M. He and W. Ren, “Attribute Reduction with Rough Set in Context-Aware Collaborative Filtering,” Chinese J. of Electronics, Vol.26, No.5, pp. 973-980, 2017.
  18. [18] X. Wu, Q. Chen, H. Liu, and C. He, “Collaborative Filtering Recommendation Algorithm Based on Representation Learning of Knowledge Graph,” Computer Engineering, Vol.44, No.2, pp. 226-232,263, 2018.
  19. [19] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet allocation,” J. of Machine Learning Research, Vol.3, pp. 993-1022, 2003.
  20. [20] C. Y. Zhang, J. L. Sun, and Y. Q. Ding, “Topic Mining for Microblog Based on MB-LDA Model,” J. of Computer Research and Development, Vol.10, No.48, pp. 1795-1802, 2011.
  21. [21] T. L. Griffiths and M. Steyvers, “Finding scientific topics,” Proc. of the National Academy of Sciences, Vol.1, No.101, pp. 5228-5235, 2004.
  22. [22] Y. Gao, H. Qi, J. Liu, and D. Y. Liu, “A Collaborative Filtering Recommendation Algorithm Combining Probabilistic Relational Models and User Grade,” J. of Computer Research and Development, Vol.9, No.45, pp. 1463-1469, 2008.
  23. [23] J. Huang, S. Rogers, and E. Joo, “Improving restaurants by extracting subtopics from yelp reviews,” iConference 2014 (Social Media Expo), 2014.
  24. [24] J. Linshi, “Personalizing Yelp Star Ratings: a Semantic Topic Modeling Approach,” Yale University, 2014.
  25. [25] D. A. V. Leeuwen and N. Brümmer, “Channel-dependent GMM and multi-class logistic: Regression models for language recognition,” 2006 IEEE Odyssey – The Speaker & Language Recognition Workshop, pp. 1-8, 2006.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Nov. 08, 2019