Abstract
Collaborative filtering (CF) plays a key role in various recommendation systems, but its effectiveness will be limited by the highly sparse user-image click-through data when CF deploys for image recommendation applications. Some existing methods apply clustering techniques to mitigate the sparseness issue. However, there is still a big room to elevate the recommendation performance, because little is known in taking both click-through data and image visual information into account. In this paper, we propose an Asynchronously Bi-Clustering (ABC) CF approach to improve the CF-based image recommendation. Our ABC approach consists of two coupled clustering solutions. Concretely, it first implements image clustering based on image click-through and visual feature, and then conducts user clustering in a low-dimensional subspace spanned by the image clusters. The final recommendation is accomplished based on both user clusters and image clusters by a similarity fusion strategy. An empirical study shows that our ABC approach is beneficial to the CF-based image recommendation, and the proposed scheme is significantly more effective than some existing methods.
Supported by NSFC of China.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
http://www.datatang.com/data/44353. The dataset was firstly used in [17].
References
Bu, J., Shen, X., Xu, B., Chen, C., He, X., Cai, D.: Improving collaborative recommendation via user-item subgroups. IEEE Trans. Knowl. Data Eng. 28(9), 2363–2375 (2016)
Cacheda, F., Formoso, V.: Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans. Web 5(1), 1–33 (2011)
Cai, X., Nie, F., Huang, H.: Multi-view K-means clustering on big data. In: International Joint Conference on Artificial Intelligence, pp. 2598–2604 (2013)
George, T., Merugu, S.: A scalable collaborative filtering framework based on co-clustering. In: IEEE International Conference on Data Mining, p. 4 (2005)
He, Y., Wu, J., Wang, H.: Clickthrough refinement for improved graph ranking. In: International Joint Conference on Neural Networks, pp. 3288–3295 (2017)
Herlocker, J.: Clustering items for collaborative filtering. In: Proceedings of the ACM SIGIR Workshop on Recommender Systems (2001)
Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Liu, L.: A sparse image recommendation model using content and user preference information. In: IEEE/WIC/ACM International Conference on Web Intelligence, pp. 232–239 (2016)
Luo, X., Ouyang, Y., Xiong, Z.: Improving K-nearest-neighborhood based collaborative filtering via similarity support. Int. J. Digit. Content Technol. Its Appl. 5(7), 248–256 (2011)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: International Conference on Neural Information Processing Systems, pp. 1257–1264 (2007)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of dimensionality reduction in recommender systems. In: ACM WebKDD Workshop (2000)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computer Science (2014)
Slaney, M.: Web-scale multimedia analysis: does content matter? IEEE Multimed. 18(2), 12–15 (2011)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Hindawi Publishing Corporation (2009)
Wang, J., Vries, A.P.D., Reinders, M.J.T.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion, pp. 501–508 (2006)
Wu, J., He, Y., Guo, X., Zhang, Y., Zhao, N.: Heterogeneous manifold ranking for image retrieval. IEEE Access PP(99), 1 (2017)
Wu, J., Shen, H., Li, Y.D., Xiao, Z.B., Lu, M.Y., Wang, C.L.: Learning a hybrid similarity measure for image retrieval. Pattern Recognit. 46(11), 2927–2939 (2013)
Xue, G.R., et al.: Scalable collaborative filtering using cluster-based smoothing. In: SIGIR: International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 114–121 (2005)
Yang, Q., Chen, Y., Xue, G.R., Dai, W., Yu, Y.: Heterogeneous transfer learning for image clustering via the social web. In: Joint Conference of the Meeting of the ACL and the International Joint Conference on Natural Language Processing of the AFNLP, vol. 1, pp. 1–9 (2009)
Acknowledgments
This work is supported by NSFC61671048 and NSFC61672088 and NSFC61790575.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jia, Y., Li, Y., Wu, J. (2018). Asynchronous Bi-clustering for Image Recommendation. In: Xu, Z., Gao, X., Miao, Q., Zhang, Y., Bu, J. (eds) Big Data. Big Data 2018. Communications in Computer and Information Science, vol 945. Springer, Singapore. https://doi.org/10.1007/978-981-13-2922-7_28
Download citation
DOI: https://doi.org/10.1007/978-981-13-2922-7_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2921-0
Online ISBN: 978-981-13-2922-7
eBook Packages: Computer ScienceComputer Science (R0)
