Weiqiang Yuan
ABSTRACT
Collaborative Filtering (CF) has been widely used in many recommender systems over the past decades. Conventional CF-based methods mainly consider the ratings given to items via users and suffer from the sparsity and cold-start problems very much. Comments written by users contain much more information about item/user profiles than ratings. And a lot of comment-based methods have been developed in recent years. In this paper, we propose a fresh framework which represents item/user profiles as vectors learned from comments. We represent comments with word embedding vectors which are widely used by deep learning methods nowadays. Sufficient experiments with different datasets show that our method is feasible and much more effective for sparsity and cold-start problems than rating-only-based methods.
- Adomavicius, G. and Tuzhilin, A. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. J. IEEE transactions on knowledge and data engineering. 17, 6, 734--749. Google Scholar
Digital Library
- Bennett, J. and Lanning, S. 2007. The netflix prize. In Proceedings of KDD cup and workshop. 2007, 35.Google Scholar
- Bobadilla, J., Ortega, F., Hernando, A., et al. 2013. Recommender systems survey. J. Knowledge-Based Systems. 46, 109--132. Google ScholarDigital Library
- Lü, L., Medo, M., Yeung, C. H., et al. 2012. Recommender systems. J. Physics Reports. 519, 1, 1--49.Google ScholarCross Ref
- Ekstrand, M. D., Riedl, J. T. and Konstan, J. A. 2011. Collaborative filtering recommender systems. J. Foundations and Trends in Human-Computer Interaction. 4, 2,81--173. Google ScholarDigital Library
- Su, X. and Khoshgoftaar, T. M. 2009. A survey of collaborative filtering techniques. J. Advances in artificial intelligence. 2009, 4. Google ScholarDigital Library
- Schafer, J. B., Frankowski, D., Herlocker, J., et al. Collaborative filtering recommender systems. M. The adaptive web. Springer. Berlin Heidelberg. 2007, 291--324. Google ScholarDigital Library
- Candillier, L., Meyer, F. and Boullé, M. 2007. Comparing state-of-the-art collaborative filtering systems. International Workshop on Machine learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg. 2007, 548--562. Google ScholarDigital Library
- Shi, Y., Larson, M. and Hanjalic, A. 2014. Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. J. ACM. Computing Surveys (CSUR). 47, 1, 3. Google ScholarDigital Library
- Sarwar, B., Karypis, G., Konstan, J., et al. 2001. Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th international conference on World Wide Web. ACM. 2001, 285--295. Google ScholarDigital Library
- Koren, Y. and Bell, R. 2011. Advances in collaborative filtering. Recommender systems handbook. Springer. USA. 2011,145--186.Google Scholar
- Koren, Y., Bell, R. and Volinsky, C. 2009. Matrix factorization techniques for recommender systems. IEEE Computer. 42, 8, 30--37. Google ScholarDigital Library
- Chen, L., Chen, G. and Wang, F. 2015. Recommender systems based on user reviews: the state of the art. J. User Modeling and User-Adapted Interaction. 25, 2, 99--154. Google ScholarDigital Library
- Garcia Esparza, S., O'Mahony, M. P. and Smyth, B. 2011. A multi-criteria evaluation of a user generated content based recommender system. Presented at the 3rd Workshop on Recommender Systems and the Social Web (RSWEB-11). 5th ACM Conference on Recommender Systems. Chicago. IL. USA. (October 2011), 23--27.Google Scholar
- Wang, Y., Liu, Y. and Yu, X. 2012. Collaborative filtering with aspect-based opinion mining: A tensor factorization approach. In: Proceedings of the IEEE International Conference on Data Mining. Brussels. Belgium. IEEE Computer Society. ICDM'12, 1152--1157. Google ScholarDigital Library
- Jakob, N., Weber, S. H., Müller, M. C., et al. 2009. Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations. Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion. ACM. 2009, 57--64. Google ScholarDigital Library
- Chen, G. and Chen, L. 2014. Recommendation based on contextual opinions. International Conference on User Modeling, Adaptation, and Personalization. Springer International Publishing. 2014, 61--73.Google ScholarCross Ref
- McAuley, J. and Leskovec, J. 2013. Hidden factors and hidden topics: understanding rating dimensions with review text. Proceedings of the 7th ACM conference on Recommender systems. ACM. 2013, 165--172. Google ScholarDigital Library
- Ling, G., Lyu, M. R. and King, I. 2014. Ratings meet reviews, a combined approach to recommend. Proceedings of the 8th ACM Conference on Recommender systems. ACM. 2014, 105--112. Google ScholarDigital Library
- Mikolov, T., Chen, K., Corrado, G. et al. 2013. Efficient estimation of word representations in vector space. J. arXiv preprint. arXiv:1301.3781Google Scholar
- Le, Q. V. and Mikolov, L. 2014. Distributed Representations of Sentences and Documents. ICML. 14, 1188--1196. Google ScholarDigital Library
- Saltan, G., Wong, A. and Yang, C. S. 1975. A vector space model for automatic indexing. Communications of the ACM. 18, 11, 613--620. Google ScholarDigital Library
- Mikolov, P., Yih, W. and Zweig, G. 2013. Linguistic Regularities in Continuous Space Word Representations. HLT-NAACL. arXiv preprint. arXiv:1301.3781.Google Scholar
- Collobert, R. and Weston, J. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. Proceedings of the 25th international conference on Machine learning. ACM. 2008, 160--167. Google ScholarDigital Library
- Bengio, Y., Ducharme, R., Vincent, P., et al. 2003. A neural probabilistic language model. J. Journal of machine learning research. 2003, 3(Feb), 1137--1155. Google ScholarDigital Library
- Mikolov, P., Karafiát, M., Burget, L., et al. 2010. Recurrent neural network based language model. In INTERSPEECH 2010, 11th Annual Conference of the International Speech Communication Association. 2010,2, 1045--1048.Google ScholarCross Ref
- Lai, S., Liu, K., He, S., et al. 2015. How to generate a good word embedding? J. arXiv preprint. arXiv: 1507.05523.Google Scholar
- Lai, S. 2016. Word and Document Embeddings based on Neural Network Approaches. J. arXiv preprint. arXiv:1611.05962.Google Scholar
- Tang, D., Qin, B. and Liu, P. 2015. Learning semantic representations of users and products for document level sentiment classification. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL) and the 7th International Joint Conference on Natural Language Processing. 2015, 1014--1023.Google Scholar
- Chen, H., Sun, M., Tu, C., et al. 2016. Neural sentiment classification with user and product attention. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2016, 1650--1659.Google ScholarCross Ref
Recommendations
A Collaborative Filtering Recommendation Algorithm Based on Item Classification
PACCS '09: Proceedings of the 2009 Pacific-Asia Conference on Circuits, Communications and SystemsCollaborative filtering systems represent services of personalized that aim at predicting a user’s interest on some items available in the application systems. With the development of electronic commerce, the number of users and items grows rapidly, ...
Novel Item Recommendation by User Profile Partitioning
WI-IAT '09: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01Standard top-N collaborative recommendation algorithms are very poor at recommending relevant products to a user that are more novel than her average tastes. Our study shows that novel recommendation is difficult because standard similarity metrics ...
Improving Accuracy of Recommender System by Item Clustering
Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...
Comments