Abstract
CTR prediction plays an important role in increasing company revenue and user experience, and many efforts start with historical behavior to uncover user interest. There are two main problems with previous works: (1) When most previous works mined interests from users’ historical behaviors, they only focus on implicit or explicit interests. (2) When most previous works mined user interests through the relationship between target users and similar users, the representation of target users was not rich and accurate enough, resulting in less accurate item recommendations. Therefore the Deep User and Item Inter-Matching Network (DUIIN) is proposed in this paper to solve the above problems. First, we design Item-to-Item Network (IIN), using two different sub-networks Evolving Interest Network (EIN) and Feature Interaction Network (FIN) to mine the hidden interests and explicit interests shown by users’ historical behaviors, respectively. Then the User-to-User Network (UUN) is designed to mine user interests through the relationship between target users and similar users after representing the target users more accurately and richly. The experimental results show that the DUIIN model proposed in this paper performs better than other state-of-the-art models.
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References
Bian, W., Wu, K., Ren, L., et al.: CAN: feature co-action network for click-through rate prediction. In: WSDM 2022: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event/Tempe, AZ, USA, 21–25 February 2022 (2022)
Cheng, H.T., Koc, L., Harmsen, J., Shaked, T., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (2016)
Feng, Y., Lv, F., Shen, W., Wang, M., Sun, F., et al.: Deep session interest network for click-through rate prediction. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, 10–16 August 2019, pp. 2301–2307 (2019)
Guo, G., Ouyang, S., He, X., et al.: Dynamic item block and prediction enhancing block for sequential recommendation. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, 10–16 August 2019, pp. 1373–1379. ijcai.org (2019)
Huang, Z., Tao, M., Zhang, B.: Deep user match network for click-through rate prediction. In: SIGIR 2021: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, 11–15 July 2021, pp. 1890–1894. ACM (2021)
Juan, Y., Zhuang, Y., Chin, W., et al.: Field-aware factorization machines for CTR prediction. In: Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, 15–19 September 2016, pp. 43–50. ACM (2016)
Li, X., Wang, C., Tong, B., et al.: Deep time-aware item evolution network for click-through rate prediction. In: CIKM 2020: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, 19–23 October 2020, pp. 785–794. ACM (2020)
Long, L., Yin, Y., Huang, F.: Hierarchical attention factorization machine for CTR prediction. In: Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Virtual Event, 11–14 April 2022, Proceedings, Part II. LNCS, vol. 13246. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-00126-0_27
Lyu, Z., Dong, Y., Huo, C., et al.: Deep match to rank model for personalized click-through rate prediction. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, New York, NY, USA, 7–12 February 2020
Qu, Y., Han, C., Kan, R., et al.: Product-based neural networks for user response prediction. In: 2016 IEEE 16th International Conference on Data Mining (ICDM) (2016)
Rendle, S.: Factorization machines. In: ICDM 2010, The 10th IEEE International Conference on Data Mining, Sydney, Australia, 14–17 December 2010, pp. 995–1000. IEEE Computer Society (2010)
Song, W., Shi, C., Xiao, Z., et al.: AutoInt: automatic feature interaction learning via self-attentive neural networks. In: the 28th ACM International Conference (2019)
Wang, R., Fu, B., Fu, G., Wang, M.: Deep & cross network for ad click predictions. In: ADKDD 2017 (2017)
Xu, W., He, H., Tan, M., et al.: Deep interest with hierarchical attention network for click-through rate prediction. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, 25–30 July 2020, pp. 1905–1908. ACM (2020)
Yang, P., Xiao, Y., Zheng, W., et al.: MAN: main-auxiliary network with attentive interactions for review-based recommendation, pp. 1–16. Springer, Cham (2022)
Yi, Y., Xu, B., Shen, S., Shen, F., Zhao, J.: Operation-aware neural networks for user response prediction. Neural Networks 121, 161–168 (2020)
Yu, F., Liu, Q., Wu, S., et al.: A dynamic recurrent model for next basket recommendation. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, 17–21 July 2016 (2016)
Zhang, J., Lin, F., Yang, C., et al.: Deep multi-representational item network for CTR prediction. In: SIGIR 2022: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11–15 July 2022, pp. 2277–2281. ACM (2022)
Xiao, Z., Yang, L., Jiang, W., et al.: Deep multi-interest network for click-through rate prediction. In: CIKM 2020: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, 11–15 October 2020, pp. 2265–2268. ACM (2020)
Zhou, G., Gai, K., Zhu, X., et al.: Deep interest network for click-through rate prediction, pp. 1059–1068 (2018)
Zhou, G., Mou, N., Fan, Y., et al.: Deep interest evolution network for click-through rate prediction. Proc. AAAI Conf. Artif. Intell. 33, 5941–5948 (2019)
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Yuan, Z., Xiao, Y., Yang, P., Hao, Q., Wang, H. (2023). Deep User and Item Inter-matching Network for CTR Prediction. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13944. Springer, Cham. https://doi.org/10.1007/978-3-031-30672-3_13
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