Abstract
Recommender systems are common in e-commerce platforms in recent years. Recommender systems are able to help users find preferential items among a large amount of products so that users’ time is saved and sellers’ profits are increased. Cross-domain recommender systems aim to recommend items based on users’ different tastes across domains. While recommender systems usually suffer from the user cold-start problem that leads to unsatisfying recommendation performance, cross-domain recommendation can remedy such a problem. This article proposes a novel cross-domain recommendation model based on regression analysis, partial least squares regression (PLSR). The proposed recommendation models, PLSR-CrossRec and PLSR-Latent, are able to purely use source-domain ratings to predict the ratings for cold-start users who never rated items in the target domains. Experiments conducted on the Epinions dataset with ten various domains’ rating records demonstrate that PLSR-Latent can outperform several matrix factorization-based competing methods under a variety of cross-domain settings. The time efficiency of PLSR-Latent is also satisfactory.
- G. Adomavicius and A. Tuzhilin. 2015. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 6 (2015), 736--749. Google ScholarDigital Library
- H. Aguinis and R. K. Gottfredson. 2010. Best-practice recommendations for estimating interaction effects using moderated multiple regression. J. Org. Behav. 31, 6 (2010), 776--786.Google ScholarCross Ref
- J. Bobadilla, F. Ortega, A. Hernando, and A. Gutierrez. 2013. Recommender systems survey. Knowl.-Based Syst. 46 (2013), 109--132. Google ScholarDigital Library
- J. S. Breese, D. Heckerman, and C. Kadie. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI’98). Google ScholarDigital Library
- I. Cantador, I. Fernandez-Tobías, S. Berkovsky, and P. Cremonesi. 2015. Cross-domain recommender systems. Recommend. Syst. Handbook (2015), 919--959.Google Scholar
- Y. Chen and C. Caramanis. 2013. Noisy and missing data regression: Distribution-oblivious support recovery. In Proceedings of the 30th International Conference on Machine Learning (ICML’13). 383--391. Google ScholarDigital Library
- P. Cremonesi, A. Tripodi, and R. Turrin. 2011. Cross-domain recommender systems. In Proceedings of the IEEE 11th International Conference on Data Mining Workshops. Google ScholarDigital Library
- M. A. Efroymson. 1960. Multiple regression analysis. Math. Methods for Dig. Comput. 23 (1960), 191--203.Google Scholar
- A. Farseev, I. Samborskii, A. Filchenkov, and T.-S. Chua. 2017. Cross-domain recommendation via clustering on multi-layer graphs. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’17). 195--204. Google ScholarDigital Library
- I. Fernandez-Tobias and I. Cantador. 2015. On the use of cross-domain user preferences and personality traits in collaborative filtering. In Proceedings of the International Conference on User Modeling, Adaptation, and Personalization (UMAP’15). 343--349.Google Scholar
- X. Jia, A. Wang, X. Li, G. Xun, W. Xu, and A. Zhang. 2015. Multi-modal learning for video recommendation based on mobile application usage. In Proceedings of IEEE International Conference on Big Data (BigData’15). 837--842. Google ScholarDigital Library
- I. Jolliffe. 2002. Principal component analysis. Springer Series in Statistics 2.Google Scholar
- Y. Koren, R. Bell, and C. Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30--37. Google ScholarDigital Library
- D. D. Lee and H. S. Seung. 2001. Algorithms for non-negative matrix factorization. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS’01). Google ScholarDigital Library
- B. Li, Q. Yang, and X. Xue. 2009. Can movies and books collaborate?: Cross-domain collaborative filtering for sparsity reduction. In Proceedings of the 21st International Joint Conference on Artifical Intelligence (IJCAI’09). Google ScholarDigital Library
- B. Lika, K. Kolomvatsos, and S. Hadjiefthymiades. 2014. Facing the cold start problem in recommender systems. Expert Syst. Appl. 41, 4 (2014), 2065--2073. Google ScholarDigital Library
- R. Manne. 1987. Analysis of two partial-least-squares algorithms for multivariate calibration. Chemometr. Intell. Lab. Syst. 2, 1 (1987), 187--197.Google ScholarCross Ref
- S. Meyffret, E. Guillot, L. Medini, and F. Laforest. 2002. RED: A rich epinions dataset for recommender systems. Research Report, Universite de Lyon (2002).Google Scholar
- N. Mirbakhsh and C. X. Ling. 2015. Improving top-N recommendation for cold-start users via cross-domain information. ACM Trans. Knowl. Discov. Data 9, 4 (2015), 33. Google ScholarDigital Library
- R. J. Mooney and L. Roy. 2000. Content-based book recommending using learning for text categorization. In Proceedings of the 5th ACM conference on Digital Libraries (JCDL’00). Google ScholarDigital Library
- L. Nie, L. Zhang, Y. Yang, M. Wang, R. Hong, and T.-S. Chua. 2015. Beyond doctors: Future health prediction from multimedia and multimodal observations. In Proceedings of the 23rd ACM International Conference on Multimedia (MM’15). 591--600. Google ScholarDigital Library
- W. Pan, E. W. Xiang, N. N. Liu, and Q. Yang. 2010. Transfer learning in collaborative filtering for sparsity reduction. In Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI’10). Google ScholarDigital Library
- W. Pan, E. W. Xiang, and Q. Yang. 2012. Transfer learning in collaborative filtering with uncertain ratings. In Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI’12). Google ScholarDigital Library
- S. Qian, T. Zhang, R. Hong, and C. Xu. 2015. Cross-domain collaborative learning in social multimedia. In Proceedings of the 23rd ACM International Conference on Multimedia (MM’15). 99--108. Google ScholarDigital Library
- R. Rosipal and N. Kramer. 2006. Overview and recent advances in partial least squares. In Proceedings of the International Conference on Subspace, Latent Structure and Feature Selection. Google ScholarDigital Library
- R. Rosipal and L. J. Trejo. 2002. Kernel partial least squares regression in reproducing kernel hilbert space. J. Mach. Learn. Res. 2 (2002), 97--123. Google ScholarDigital Library
- B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web (WWW’01). Google ScholarDigital Library
- B. Shapira, L. Rokach, and S. Freilikhman. 2013. Facebook single and cross domain data for recommendation systems. User Model. User-Adapt. Interact. 23, 2 (2013), 211--247.Google ScholarCross Ref
- Y. Shi, M. Larson, and A. Hanjalic. 2011. Tags as bridges between domains: Improving recommendation with tag-induced cross-domain collaborative filtering. In Proceedings of the International Conference on User Modeling, Adaptation, and Personalization. Google ScholarDigital Library
- A. P. Singh and G. J. Gordon. 2008. Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’08). 650--658. Google ScholarDigital Library
- Q. Song, J. Cheng, and H. Lu. 2015. Incremental matrix factorization via feature space re-learning for recommender system. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys’15). 277--280. Google ScholarDigital Library
- N. Srivastava and R. Salakhutdinov. 2014. Multimodal learning with deep Boltzmann machines. J. Mach. Learn. Res. 15 (2014), 2949--2980. Google ScholarDigital Library
- H. Steck. 2013. Evaluation of recommendations: Rating-prediction and ranking. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys’13). 213--220. Google ScholarDigital Library
- A. Tiroshi, S. Berkovsky, M. A. Kaafar, T. Chen, and T. Kuflik. 2013. Cross social networks interests predictions based on graph features. In Proceedings of the 7th ACM Conference on Recommender Systems. Google ScholarDigital Library
- H. Wold. 1975. Path models with latent variables: The NIPALS approach. Quant. Sociol.: Int. Perspect. Math. Stat. Model Build. (1975), 307--357.Google Scholar
- Y. Zhang, B. Cao, and D.-Y. Yeung. 2012. Multi-domain collaborative filtering. In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI’12).Google Scholar
- X. W. Zhao, Y. Guo, Y. He, H. Jiang, Y. Wu, and X. Li. 2014. We know what you want to buy: A demographic-based system for product recommendation on microblogs. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’14). 1935--1944. Google ScholarDigital Library
- F. Zhuang, P. Luo, H. Xiong, Y. Xiong, Q. He, and Z. Shi. 2010. Cross-domain learning from multiple sources: A consensus regularization perspective. IEEE Trans. Knowl. Data Eng. 22, 12 (2010), 1664--1678. Google ScholarDigital Library
Index Terms
- A Cross-Domain Recommendation Mechanism for Cold-Start Users Based on Partial Least Squares Regression
Recommendations
Improving Top-N Recommendation for Cold-Start Users via Cross-Domain Information
Making accurate recommendations for cold-start users is a challenging yet important problem in recommendation systems. Including more information from other domains is a natural solution to improve the recommendations. However, most previous work in ...
Cross-Domain Recommendation for Cold-Start Users via Neighborhood Based Feature Mapping
Database Systems for Advanced ApplicationsAbstractTraditional Collaborative Filtering (CF) models mainly focus on predicting a user’s preference to the items in a single domain such as the movie domain or the music domain. A major challenge for such models is the data sparsity problem, and ...
Naïve filterbots for robust cold-start recommendations
KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data miningThe goal of a recommender system is to suggest items of interest to a user based on historical behavior of a community of users. Given detailed enough history, item-based collaborative filtering (CF) often performs as well or better than almost any ...
Comments