Abstract
User interest modeling is crucial for personalized news recommendation. Existing personalized news recommendation methods usually take the news data as the minimum interest modeling unit when modeling user’s interests. They ignored the low-level and high-level signals from user’s behaviors. In this paper, we propose a news recommendation method combined with multi-level and multi-interest user interest modeling, named MMRN. In contrast to existing methods, our MMRN model captures user interest at multiple levels, including word-level, news-level and higher-levels, then learns multiple user interests vectors in each level. Extensive experiments on a real-world dataset show our method can effectively improve the recommendation effect.
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References
Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th International Conference on World Wide Web, pp. 271–280 (2007)
Okura, S., Tagami, Y., Ono, S., Tajima, A.: Embedding-based news recommendation for millions of users. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1933–1942 (2017)
An, M., Wu, F., Wu, C., Zhang, K., Liu, Z., Xie, X.: Neural news recommendation with long-and short-term user representations. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 336–345 (2019)
Wang, H., Zhang, F., Xie, X., Guo, M.: DKN: deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 World Wide Web Conference, pp. 1835–1844 (2018)
Wu, C., Wu, F., Ge, S., Qi, T., Huang, Y., Xie, X.: Neural news recommendation with multi-head self-attention. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). pp. 6389–6394 (2019)
Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, pp. 31–40 (2010)
Phelan, O., McCarthy, K., Smyth, B.: Using twitter to recommend real-time topical news. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 385–388 (2009)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)
Qi, T., et al.: HieRec: hierarchical user interest modeling for personalized news recommendation. arXiv preprint arXiv:2106.04408 (2021)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Li, C., et al.: Multi-interest network with dynamic routing for recommendation at Tmall. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2615–2623 (2019)
Chang, J., et al.: Sequential recommendation with graph neural networks. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 378–387 (2021)
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Advances in Neural Information Processing Systems. vol. 30 (2017)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Wu, F., et al.: MIND: a large-scale dataset for news recommendation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3597–3606 (2020)
Sarzynska-Wawer, J., et al.: Detecting formal thought disorder by deep contextualized word representations. Psychiatry Res. 304, 114135 (2021)
Wu, C., Wu, F., An, M., Huang, J., Huang, Y., Xie, X.: NPA: neural news recommendation with personalized attention. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2576–2584 (2019)
Wang, H., Wu, F., Liu, Z., Xie, X.: Fine-grained interest matching for neural news recommendation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 836–845 (2020)
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Hou, Y., Ouyang, Y., Liu, Z., Han, F., Rong, W., Xiong, Z. (2023). Multi-level and Multi-interest User Interest Modeling for News Recommendation. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14119. Springer, Cham. https://doi.org/10.1007/978-3-031-40289-0_16
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DOI: https://doi.org/10.1007/978-3-031-40289-0_16
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