Abstract
Recommender systems usually need to compare user interests and item characteristics in the context of large user and item space, making hashing based algorithms a promising strategy to speed up recommendation. Existing hashing based recommendation methods only model the users and items and dealing with the matrix data, e.g., user-item rating matrix. In practice, recommendation scenarios can be rather complex, e.g., collaborative retrieval and personalized tag recommendation. The above scenarios generally need fast search for one type of entities (target entities) using multiple types of entities (source entities). The resulting three or higher order tensor data makes conventional hashing algorithms fail for the above scenarios. In this paper, a novel hashing method is accordingly proposed to solve the above problem, where the tensor data is approached by properly designing the similarities between the source entities and target entities in Hamming space. Besides, operator matrices are further developed to explore the relationship between different types of source entities, resulting in auxiliary codes for source entities. Extensive experiments on two tasks, i.e., personalized tag recommendation and collaborative retrieval, demonstrate that the proposed method performs well for tensor data.
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References
Gionis, A., Indyk, P., Motwani, R., et al.: Similarity search in high dimensions via hashing. In: International Conference on Very Large Data Bases, vol. 99, pp. 518–529 (1999)
Gong, Y., Lazebnik, S.: Iterative quantization: A procrustean approach to learning binary codes. In: Computer Vision and Pattern Recognition, pp. 817–824 (2011)
Grauman, K., Fergus, R.: Learning binary hash codes for large-scale image search. In: Cipolla, R., Battiato, S., Farinella, G.M. (eds.) Machine Learning for Computer Vision. SCI, vol. 411, pp. 55–93. Springer, Heidelberg (2013)
Hsiao, K.J., Kulesza, A., Hero, A.: Social collaborative retrieval. In: International Conference on Web Search and Data Mining, pp. 293–302 (2014)
Koenigstein, N., Ram, P., Shavitt, Y.: Efficient retrieval of recommendations in a matrix factorization framework. In: International Conference on Information and Knowledge Management, pp. 535–544 (2012)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. Internet Computing 7(1), 76–80 (2003)
Liu, T., Moore, A.W., Yang, K., Gray, A.G.: An investigation of practical approximate nearest neighbor algorithms. In: Advances in Neural Information Processing Systems, pp. 825–832 (2004)
Ntoutsi, E., Stefanidis, K., Nørvåg, K., Kriegel, H.-P.: Fast group recommendations by applying user clustering. In: Atzeni, P., Cheung, D., Ram, S. (eds.) ER 2012 Main Conference 2012. LNCS, vol. 7532, pp. 126–140. Springer, Heidelberg (2012)
Ou, M., Cui, P., Wang, F., Wang, J., Zhu, W., Yang, S.: Comparing apples to oranges: a scalable solution with heterogeneous hashing. In: International Conference on Knowledge Discovery and Data Mining, pp. 230–238 (2013)
Rendle, S., Balby Marinho, L., Nanopoulos, A., Schmidt-Thieme, L.: Learning optimal ranking with tensor factorization for tag recommendation. In: International Conference on Knowledge Discovery and Data Mining, pp. 727–736 (2009)
Wang, J., Shen, H.T., Song, J., Ji, J.: Hashing for similarity search: A survey. arXiv preprint arXiv:1408.2927 (2014)
Wang, Q., Ruan, L., Zhang, Z., Si, L.: Learning compact hashing codes for efficient tag completion and prediction. In: International Conference on Information & Knowledge Management, pp. 1789–1794 (2013)
Wang, Q., Shen, B., Wang, S., Li, L., Si, L.: Binary codes embedding for fast image tagging with incomplete labels. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part II. LNCS, vol. 8690, pp. 425–439. Springer, Heidelberg (2014)
Wang, Q., Si, L., Zhang, D.: Learning to hash with partial tags: Exploring correlation between tags and hashing bits for large scale image retrieval. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part III. LNCS, vol. 8691, pp. 378–392. Springer, Heidelberg (2014)
Wei, Y., Song, Y., Zhen, Y., Liu, B., Yang, Q.: Scalable heterogeneous translated hashing. In: International Conference on Knowledge Discovery and Data Mining, pp. 791–800 (2014)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advances in Neural Information Processing Systems, pp. 1753–1760 (2009)
Weston, J., Wang, C., Weiss, R., Berenzweig, A.: Latent collaborative retrieval. International Conference on Machine Learning (2012)
Yin, H., Cui, B., Chen, L., Hu, Z., Huang, Z.: A temporal context-aware model for user behavior modeling in social media systems, pp. 1543–1554 (2014)
Yin, H., Cui, B., Chen, L., Hu, Z., Zhou, X.: Dynamic user modeling in social media systems. ACM Transactions on Information Systems 33(3), 10 (2015)
Yin, H., Sun, Y., Cui, B., Hu, Z., Chen, L.: Lcars: a location-content-aware recommender system. In: ACM SIGMOD International Conference on Knowledge Discovery and Data Mining, pp. 221–229 (2013)
Zhai, D., Chang, H., Zhen, Y., Liu, X., Chen, X., Gao, W.: Parametric local multimodal hashing for cross-view similarity search. In: International Joint Conference on Artificial Intelligence, pp. 2754–2760 (2013)
Zhang, D., Yang, G., Hu, Y., Jin, Z., Cai, D., He, X.: A unified approximate nearest neighbor search scheme by combining data structure and hashing. In: International Joint Conference on Artificial Intelligence, pp. 681–687 (2013)
Zhang, D., Wang, J., Cai, D., Lu, J.: Self-taught hashing for fast similarity search. In: International Conference on Research and Development in Information Retrieval, pp. 18–25 (2010)
Zhang, Z., Wang, Q., Ruan, L., Si, L.: Preference preserving hashing for efficient recommendation. In: International Conference on Research & Development in Information Retrieval, pp. 183–192 (2014)
Zhou, K., Zha, H.: Learning binary codes for collaborative filtering. In: International Conference on Knowledge Discovery and Data Mining, pp. 498–506 (2012)
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Yin, Q., Wu, S., Wang, L. (2015). Learning to Hash for Recommendation with Tensor Data. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_24
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DOI: https://doi.org/10.1007/978-3-319-25255-1_24
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