Abstract
It is already well known that recommender systems usually suffer from data sparsity issue of user-item interactions. However, representation learning can efficiently measure correlations between objects, which presents an unprecedented opportunity to alleviate this issue. In this paper, we propose a new distributional vector space model, Tag2Vec, for capturing meaningful relationships of users and items to improve the performance of recommender systems. First, we represent users and items as vectors respectively using tag embedding. With this innovative representation, the semantic relationships between users and items can be captured. To be specific, tag2vec learns representations of users and items in low-dimensional space from user-tag-item interactions using the skip-gram model. Second, we measure similarity between both users and items, and collaborative filtering can then be performed in the learned embedding space. To evaluate the performance of Tag2Vec, we conduct extensive experiments with two real world datasets for Top-N recommendation tasks. The results demonstrate that our proposed method significantly outperforms existing approaches.
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References
Katarya, R., Verma, O.P.: Privacy-preserving and secure recommender system enhance with K-NN and social tagging. In: 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud), pp. 52–57. IEEE (2017). https://doi.org/10.1109/CSCloud.2017.24
Yu, H., Li, J.: Algorithm to solve the cold-start problem in new item recommendations. J. Softw. 26(6), 1395–1408 (2015). https://doi.org/10.13328/j.cnki.jos.004587
Mistry, O., Sen, S.: Tag recommendation for social bookmarking: probabilistic approaches. Multiagent Grid Syst. 8(2), 143–163 (2012). https://doi.org/10.3233/MGS-2012-0190
Yoshua, B., Aaron, C., Pascal, V.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013). https://doi.org/10.1109/TPAMI.2013.50
Ortega, F., Hernando, A., Bobadilla, J., Kang, J.H.: Recommending items to group of users using matrix factorization based collaborative filtering. Inf. Sci. 345(C), 313–324 (2016). https://doi.org/10.1016/j.ins.2016.01.083
Lin, J., Sugiyama, K., Kan, M.Y., Chua, T.S.: Addressing cold-start in app recommendation: latent user models constructed from twitter followers (2013). https://doi.org/10.1145/2484028.2484035
Georgiev, K., Nakov, P.: A non-IID framework for collaborative filtering with restricted boltzmann machines. In: Proceedings of the 30th International Conference on International Conference on Machine Learning - vol. 28, ICML 2013, pp. III–1148–III–1156. JMLR.org (2013). http://dl.acm.org/citation.cfm?id=3042817.3043065
Salakhutdinov, R., Mnih, A., Hinton, G.E.: Restricted Boltzmann machines for collaborative filtering. In: International Conference on Machine Learning (2007). https://doi.org/10.1145/1273496.1273596
Saveski, M., Mantrach, A.: Item cold-start recommendations: learning local collective embeddings (2014). https://doi.org/10.1145/2645710.2645751
He, X., Kan, M.Y., Xie, P., Xiao, C.: Comment-based multi-view clustering of web 2.0 items. In: International Conference on World Wide Web (2014). https://doi.org/10.1145/2566486.2567975
Costa, F., Dolog, P.: Hybrid learning model with barzilai-borwein optimization for context-aware recommendations. In: Proceedings of the Thirty-First International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018, Melbourne, Florida, USA. May 21–23 2018, pp. 456–461 (2018). https://aaai.org/ocs/index.php/FLAIRS/FLAIRS18/paper/view/17630
Acknowledgment
The work is supported by the Beijing Natural Science Foundation (No. 4192008) and the Beijing Municipal Education Commission (No. KM201710005023).
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He, M., Yao, K., Yang, P., Yao, Y. (2020). Tag2Vec: Tag Embedding for Top-N Recommendation. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_18
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DOI: https://doi.org/10.1007/978-3-030-32591-6_18
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