Abstract
In this paper, we propose a novel Cross-Domain Collaborative Filtering (CDCF) algorithm termed Low-rank and Sparse Cross-Domain (LSCD) recommendation algorithm. Different from most of the CDCF algorithms which tri-factorize the rating matrix of each domain into three low dimensional matrices, LSCD extracts a user and an item latent feature matrix for each domain respectively. Besides, in order to improve the performance of recommendations among correlated domains by transferring knowledge and uncorrelated domains by differentiating features in different domains, the features of users are separated into shared and domain-specific parts adaptively. Specifically, a low-rank matrix is used to capture the shared feature subspace of users and a sparse matrix is used to characterize the discriminative features in each specific domain. Extensive experiments on two real-world datasets have been conducted to confirm that the proposed algorithm transfers knowledge in a better way to improve the quality of recommendation and outperforms the state-of-the-art recommendation algorithms.
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Notes
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Source code and datasets are available at https://github.com/sysulawliet/LSCD.
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References
Zheng, B., Su, H., Zheng, K., Zhou, X.: Landmark-based route recommendation with crowd intelligence. Data Sci. Eng. 1(2), 86–100 (2016)
Zhao, Z.L., Wang, C.D., Wan, Y.Y., Lai, J.H.: Recommendation in feature space sphere. Electron. Commer. Res. Appl. 26, 109–118 (2017)
Hu, Q.Y., Zhao, Z.L., Wang, C.D., Lai, J.H.: An item orientated recommendation algorithm from the multi-view perspective. Neurocomputing 269, 261–272 (2017)
Jannach, D., Zanker, M., Felfering, A., Friedrich, G.: Recommender Systems: An Introduction. Addison Wesley Publishing, Boston (2013)
Zhao, Z.L., Wang, C.D., Lai, J.H.: AUI&GIV: recommendation with asymmetric user influence and global importance value. PLoS ONE 11(2), e0147944 (2016)
Allab, K., Labiod, L., Nadif, M.: A Semi-NMF-PCA unified framework for data clustering. IEEE Trans. Knowl. Data Eng. 29(1), 2–16 (2017)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2007)
Guo, G., Zhang, J., Thalmann, D.: Merging trust in collaborative filtering to alleviate data sparsity and cold start. Knowl.-Based Syst. 57, 57–68 (2014)
Tan, B., Song, Y., Zhong, E., Yang, Q.: Transitive transfer learning. In: KDD, pp. 1155–1164 (2015)
Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM, pp. 287–296 (2011)
Wei, S., Zheng, X., Chen, D., Chen, C.: A hybrid approach for movie recommendation via tags and ratings. Electron. Commer. Res. Appl. 18, 83–94 (2016)
Xin, X., Liu, Z., Lin, C.Y., Huang, H., Wei, X., Guo, P.: Cross-domain collaborative filtering with review text. In: IJCAI, pp. 1827–1834 (2015)
McAuley, J.J., Targett, C., Shi, Q., van den Hengel, A.: Image-based recommendations on styles and substitutes. In: SIGIR, pp. 43–52 (2016)
Song, T., Peng, Z., Wang, S., Fu, W., Hong, X., Yu, P.S.: Review-based cross-domain recommendation through joint tensor factorization. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10177, pp. 525–540. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55753-3_33
Moreno, O., Shapira, B., Rokach, L., Shani, G.: TALMUD: transfer learning for multiple domains. In: CIKM, pp. 425–434 (2012)
Hu, L., Cao, J., Xu, G., Cao, L., Gu, Z., Zhu, C.: Personalized recommendation via cross-domain triadic factorization. In: WWW, pp. 595–606 (2013)
Gao, S., Luo, H., Chen, D., Li, S., Gallinari, P., Guo, J.: Cross-domain recommendation via cluster-level latent factor model. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013. LNCS (LNAI), vol. 8189, pp. 161–176. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40991-2_11
Liu, Y.F., Hsu, C.Y., Wu, S.H.: Non-linear cross-domain collaborative filtering via hyper-structure transfer. In: ICML, pp. 1190–1198 (2015)
Chen, J., Zhou, J., Ye, J.: Integrating low-rank and group-sparse structures for robust multi-task learning. In: KDD, pp. 42–50 (2011)
Candes, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis. J. ACM 58(3), 1–39 (2011)
Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: KDD, pp. 650–658 (2008)
Iwata, T., Takeuchi, K.: Cross-domain recommendation without shared users or items by sharing latent vector distributions. In: AISTATS, pp. 379–387 (2015)
Ren, S., Gao, S., Liao, J., Guo, J.: Improving cross-domain recommendation through probabilistic cluster-level latent factor model. In: AAAI, pp. 4200–4201 (2015)
Acknowledgments
This work was supported by NSFC (61502543 & 61602189), Guangdong Natural Science Funds for Distinguished Young Scholar (2016A030306014), the Ph.D. Start-up Fund of Natural Science Foundation of Guangdong Province, China (2016A030310457), and Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (2016TQ03X542).
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Zhao, ZL., Huang, L., Wang, CD., Huang, D. (2018). Low-Rank and Sparse Cross-Domain Recommendation Algorithm. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_10
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