Abstract
With the increasingly complexity and dynamically of information, recommendation system has been a key solution to alleviate the problem of information overloaded. Most recommender system models users’ preferences toward items based on users’ historical implicit feedback with item (e.g., product purchase history, browsing logs, etc.). They typically make recommendation for a target user based on her profiles only (e.g., the user’s previous activities), ignoring the existence of other valuable information on items such as the visual features of images corresponding to the items. As a downside, it may limit the performance of recommender systems to some extent. This paper proposes a joint prediction model (visual-SLIM) which extends SLIM method with visual information to predict people’s preference. The proposed approach automatically generates the missing items scores for a target user by aggregating observed user-item interaction matrix and learning linear regression model with items’ visual information. It would not only improve performance of the model, but also do help to better analysis of the effects of visual information on user’s opinions. Extensive experiments conducted on the real-world dataset of the Amazon have demonstrated the effectiveness of our proposed model.
This work is supported by the National Natural Science Foundation of China (No. 61772170, 61472115), the National Key Research and Development Program of China (No. 2017YFB0803301) and the Fundamental Research Funds for the Central Universities (No. JZ2017YYPY0234).
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References
Bao, Y., Fang, H., Zhang, J.: TopicMF: simultaneously exploiting ratings and reviews for recommendation. In: AAAI, vol. 14, pp. 2–8 (2014)
Brand, M.: Fast low-rank modifications of the thin singular value decomposition. Linear Algebra Appl. 415(1), 20–30 (2006)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann Publishers Inc. (1998)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 39–46. ACM (2010)
Friedland, S., Torokhti, A.: Generalized rank-constrained matrix approximations. SIAM J. Matrix Anal. Appl. 29(2), 656–659 (2007)
He, R., McAuley, J.: VBPR: visual Bayesian personalized ranking from implicit feedback. In: AAAI, pp. 144–150 (2016)
He, X., Zhang, H., Kan, M.Y., Chua, T.S.: Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 549–558. ACM (2016)
Hong, R., Hu, Z., Wang, R., Wang, M., Tao, D.: Multi-view object retrieval via multi-scale topic models. IEEE Trans. Image Process. 25(12), 5814–5827 (2016)
Hong, R., Zhang, L., Tao, D.: Unified photo enhancement by discovering aesthetic communities from Flickr. IEEE Trans. Image Process. 25(3), 1124–1135 (2016)
Hong, R., Zhang, L., Zhang, C., Zimmermann, R.: Flickr circles: aesthetic tendency discovery by multi-view regularized topic modeling. IEEE Trans. Multimed. 18(8), 1555–1567 (2016)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 263–272. IEEE (2008)
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)
Karatzoglou, A., Amatriain, X., Baltrunas, L., Oliver, N.: Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 79–86. ACM (2010)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Lei, C., Liu, D., Li, W., Zha, Z.J., Li, H.: Comparative deep learning of hybrid representations for image recommendations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2545–2553 (2016)
Liu, J., et al.: Multi-scale triplet CNN for person re-identification. In: Proceedings of the 2016 ACM on Multimedia Conference, pp. 192–196. ACM (2016)
McAuley, J., Leskovec, J.: Image labeling on a network: using social-network metadata for image classification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 828–841. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33765-9_59
McAuley, J., Targett, C., Shi, Q., Van Den Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43–52. ACM (2015)
Merialdo, A.K.B.: Clustering for collaborative filtering applications. Intell. Image Process. Data Anal. Inf. Retr. 3, 199 (1999)
Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conference on Digital Libraries, pp. 195–204. ACM (2000)
Ning, X., Karypis, G.: Slim: Sparse linear methods for top-n recommender systems. In: IEEE 11th International Conference on Data Mining (ICDM), pp. 497–506. IEEE (2011)
Ning, X., Karypis, G.: Sparse linear methods with side information for top-n recommendations. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 155–162. ACM (2012)
OâĂŹConnor, M., Herlocker, J.: Clustering items for collaborative filtering. In: Proceedings of the ACM SIGIR Workshop on Recommender Systems, vol. 128. UC Berkeley (1999)
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_10
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook, 1st edn. Springer, Heidelberg (2010). https://doi.org/10.1007/978-0-387-85820-3
Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th International Conference on Machine Learning, pp. 880–887. ACM (2008)
Savia, E., Puolamaki, K., Sinkkonen, J., Kaski, S.: Two-way latent grouping model for user preference prediction. arXiv preprint arXiv:1207.1414 (2012)
Sindhwani, V., Bucak, S.S., Hu, J., Mojsilovic, A.: One-class matrix completion with low-density factorizations. In: IEEE 10th International Conference on Data Mining (ICDM), pp. 1055–1060. IEEE (2010)
Sondermann, D.: Best approximate solutions to matrix equations under rank restrictions. Statistische Hefte 27(1), 57 (1986)
Tan, Z., He, L.: An efficient similarity measure for user-based collaborative filtering recommender systems inspired by the physical resonance principle. IEEE Access 5, 27211–27228 (2017)
Wu, J.: Binomial matrix factorization for discrete collaborative filtering. In: Ninth IEEE International Conference on Data Mining, ICDM 2009, pp. 1046–1051. IEEE (2009)
Yi, X., Hong, L., Zhong, E., Liu, N.N., Rajan, S.: Beyond clicks: dwell time for personalization. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 113–120. ACM (2014)
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Chen, S., Xue, F., Zhang, H. (2018). Visual-SLIM: Integrated Sparse Linear Model with Visual Features for Personalized Recommendation. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11164. Springer, Cham. https://doi.org/10.1007/978-3-030-00776-8_12
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