Learning to Recommend with Negative Ratings Based on Factorization Machine
Rating prediction is an important problem for recommender systems. The task is to predict the rating for an item that a user would give. Most of the existing algorithms for the task ignore the effect of negative ratings rated by users on items, but the negative ratings have a significant impact on users’ purchasing decisions in practice. In this paper, we present a rating prediction algorithm based on factorization machines that consider the effect of negative ratings inspired by Loss Aversion theory. The aim of this paper is to develop a concave and a convex negative disgust function to evaluate the negative ratings respectively. Experiments are conducted on MovieLens dataset. The experimental results demonstrate the effectiveness of the proposed methods by comparing with other four the state-of-the-art approaches. The negative ratings showed much importance in the accuracy of ratings predictions.
[1] Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109-2.
[2] Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems, 74, 12-32.
[3] Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009, 4.
[4] Huang, Z., Chen, H., &Zeng, D. (2004). Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems (TOIS), 22(1), 116-142.
[5] Rendle, S. (2010, December). Factorization machines. In 2010 IEEE International Conference on Data Mining (pp. 995-1000). IEEE.
[6] Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. In Advances in neural information processing systems (pp. 556-562).
[7] Lee, J., Lee, D., Lee, Y. C., Hwang, W. S., & Kim, S. W. (2016). Improving the accuracy of top-N recommendation using a preference model. Information Sciences, 348, 290-304.
[8] Haihong, E., Li, Y., Zhao, X., Song, M., & Song, J. (2016, January). A General Rating Recommended Weight-Aware Model for Recommendation System. In International.
[9] Ma, H., Lyu, M. R., & King, I. (2009, October). Learning to recommend with trust and distrust relationships. In Proceedings of the third ACM conference on Recommender systems (pp. 189-196). ACM.
[10] Forsati, R., Barjasteh, I., Masrour, F., Esfahanian, A. H., & Radha, H. (2015, September). Pushtrust: An efficient recommendation algorithm by leveraging trust and distrust relations. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 51-58). ACM.
[11] "Feature Engineering: How to transform variables and create new ones?" Retrieved 2016-01-10. https://www.analyticsvidhya.com/blog/2016/01/guide-data-exploration/
[12] Sankar, D. M. and Sarkar, S. (2012) Rating Prediction using Preference Relations Based on Matrix FactMod Workshop in UMAP July 2012.
[13] Tversky, A., &Kahneman, D. (1991). Loss aversion in riskless choice: A reference-dependent model. The quarterly journal of economics, 1039-1061.
[14] Massa, P., & Avesani, P. (2007). Trust metrics on controversial users: Balancing between tyranny of the majority. International Journal on Semantic Web and Information Systems (IJSWIS), 3(1), 39-64.
[15] Steck, H. (2013, October). Evaluation of recommendations: rating-prediction and ranking. In Proceedings of the 7th ACM Conference on Recommender Systems (pp. 213-220). ACM.
[16] Rendle, S., & Schmidt-Thieme, L. (2008, October). Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In Proceedings of the 2008 ACM conference on Recommender systems (pp. 251-258). ACM.
[17] Koren, Y. (2008, August). Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 426-434). ACM.
[18] Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001, April). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285-295). ACM.
[19] Kohavi, R. (1995, August). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai (Vol. 14, No. 2, pp. 1137-1145).
[20] Lam, X. N., Vu, T., Le, T. D., & Duong, A. D. (2008, January). Addressing cold-start problem in recommendation systems. In Proceedings of the 2nd international conference on Ubiquitous information management and communication (pp. 208-211). ACM.
[1] Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109-2.
[2] Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems, 74, 12-32.
[3] Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009, 4.
[4] Huang, Z., Chen, H., &Zeng, D. (2004). Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems (TOIS), 22(1), 116-142.
[5] Rendle, S. (2010, December). Factorization machines. In 2010 IEEE International Conference on Data Mining (pp. 995-1000). IEEE.
[6] Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. In Advances in neural information processing systems (pp. 556-562).
[7] Lee, J., Lee, D., Lee, Y. C., Hwang, W. S., & Kim, S. W. (2016). Improving the accuracy of top-N recommendation using a preference model. Information Sciences, 348, 290-304.
[8] Haihong, E., Li, Y., Zhao, X., Song, M., & Song, J. (2016, January). A General Rating Recommended Weight-Aware Model for Recommendation System. In International.
[9] Ma, H., Lyu, M. R., & King, I. (2009, October). Learning to recommend with trust and distrust relationships. In Proceedings of the third ACM conference on Recommender systems (pp. 189-196). ACM.
[10] Forsati, R., Barjasteh, I., Masrour, F., Esfahanian, A. H., & Radha, H. (2015, September). Pushtrust: An efficient recommendation algorithm by leveraging trust and distrust relations. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 51-58). ACM.
[11] "Feature Engineering: How to transform variables and create new ones?" Retrieved 2016-01-10. https://www.analyticsvidhya.com/blog/2016/01/guide-data-exploration/
[12] Sankar, D. M. and Sarkar, S. (2012) Rating Prediction using Preference Relations Based on Matrix FactMod Workshop in UMAP July 2012.
[13] Tversky, A., &Kahneman, D. (1991). Loss aversion in riskless choice: A reference-dependent model. The quarterly journal of economics, 1039-1061.
[14] Massa, P., & Avesani, P. (2007). Trust metrics on controversial users: Balancing between tyranny of the majority. International Journal on Semantic Web and Information Systems (IJSWIS), 3(1), 39-64.
[15] Steck, H. (2013, October). Evaluation of recommendations: rating-prediction and ranking. In Proceedings of the 7th ACM Conference on Recommender Systems (pp. 213-220). ACM.
[16] Rendle, S., & Schmidt-Thieme, L. (2008, October). Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In Proceedings of the 2008 ACM conference on Recommender systems (pp. 251-258). ACM.
[17] Koren, Y. (2008, August). Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 426-434). ACM.
[18] Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001, April). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285-295). ACM.
[19] Kohavi, R. (1995, August). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai (Vol. 14, No. 2, pp. 1137-1145).
[20] Lam, X. N., Vu, T., Le, T. D., & Duong, A. D. (2008, January). Addressing cold-start problem in recommendation systems. In Proceedings of the 2nd international conference on Ubiquitous information management and communication (pp. 208-211). ACM.
@article{"International Journal of Business, Human and Social Sciences:76256", author = "Caihong Sun and Xizi Zhang", title = "Learning to Recommend with Negative Ratings Based on Factorization Machine", abstract = "Rating prediction is an important problem for recommender systems. The task is to predict the rating for an item that a user would give. Most of the existing algorithms for the task ignore the effect of negative ratings rated by users on items, but the negative ratings have a significant impact on users’ purchasing decisions in practice. In this paper, we present a rating prediction algorithm based on factorization machines that consider the effect of negative ratings inspired by Loss Aversion theory. The aim of this paper is to develop a concave and a convex negative disgust function to evaluate the negative ratings respectively. Experiments are conducted on MovieLens dataset. The experimental results demonstrate the effectiveness of the proposed methods by comparing with other four the state-of-the-art approaches. The negative ratings showed much importance in the accuracy of ratings predictions.
", keywords = "Factorization machines, feature engineering, negative ratings, recommendation systems.", volume = "11", number = "9", pages = "2235-7", }
