Abstract
As deep learning (DL) technologies have developed rapidly, many new techniques have become available for recommender systems. Yet, there is very little research addressing how users’ feedback for particular items (such as ratings) can affect recommendations. This feedback can assist in building more fine-grained user profiles, as not all raw clicks will truly reflect a user’s preference. The challenge of encoding such records, which are typically prohibitively long, also prevents research from considering using the whole click history to learn representations. To address these challenges, we propose MARF, a novel model for click prediction. Specifically, we construct fine-grained user representations (by considering both the multiple items browsed, and user’s feedback on them) and item representations (by considering browsing histories from multiple users, and their feedback). Moreover, the flexible up-down strategy is designed to avoid loading incomplete or overloaded historical information by selecting representative users/items based on their feedback records. A comprehensive evaluation on three large scale real-world benchmark datasets, showing that MARF significantly outperforms a variety of state-of-the-art solutions. Furthermore, MARF model is evaluated through an ablation study that validates the contribution of each component. As a final demonstration, we show how MARF can be used for cross-domain recommendation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aoyama, H.: A study of stratified random sampling. Ann. Inst. Stat. Math. 6(1), 1–36 (1954)
Cheng, H., et al.: Wide & deep learning for recommender systems. In: Karatzoglou, A., et al. (eds.) Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, DLRS@RecSys 2016, Boston, MA, USA, 15 September 2016, pp. 7–10. ACM (2016)
Cheng, W., Shen, Y., Huang, L.: Adaptive factorization network: learning adaptive-order feature interactions. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, 7–12 February 2020, pp. 3609–3616. AAAI Press (2020)
Covington, P., Adams, J., Sargin, E.: Deep neural networks for youtube recommendations. In: Sen, S., Geyer, W., Freyne, J., Castells, P. (eds.) Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, 15–19 September 2016, pp. 191–198. ACM (2016)
Feng, Y., et al.: Deep session interest network for click-through rate prediction. In: Kraus, S. (ed.) Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, 10–16 August 2019, pp. 2301–2307 (2019). ijcai.org
Fu, W., Peng, Z., Wang, S., Xu, Y., Li, J.: Deeply fusing reviews and contents for cold start users in cross-domain recommendation systems. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, Honolulu, Hawaii, USA, 27 January - 1 February 2019, pp. 94–101. AAAI Press (2019)
Graepel, T., Candela, J.Q., Borchert, T., Herbrich, R.: Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft’s bing search engine. In: Fürnkranz, J., Joachims, T. (eds.) Proceedings of the 27th International Conference on Machine Learning, ICML 2010, Haifa, Israel, 21–24 June 2010, pp. 13–20. Omnipress (2010)
Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: Deepfm: a factorization-machine based neural network for CTR prediction. In: Sierra, C. (ed.) Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 19–25 August 2017, pp. 1725–1731 (2017). ijcai.org
He, R., McAuley, J.J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Bourdeau, J., Hendler, J., Nkambou, R., Horrocks, I., Zhao, B.Y. (eds.) Proceedings of the 25th International Conference on World Wide Web, WWW 2016, Montreal, Canada, 11–15 April 2016, pp. 507–517. ACM (2016)
He, X., Chua, T.: Neural factorization machines for sparse predictive analytics. In: Kando, N., Sakai, T., Joho, H., Li, H., de Vries, A.P., White, R.W. (eds.) Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, 7–11 August 2017, pp. 355–364. ACM (2017)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.: Neural collaborative filtering. In: Barrett, R., Cummings, R., Agichtein, E., Gabrilovich, E. (eds.) Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, 3–7 April 2017, pp. 173–182. ACM (2017)
Huang, T., Zhang, Z., Zhang, J.: Fibinet: combining feature importance and bilinear feature interaction for click-through rate prediction. In: Bogers, T., Said, A., Brusilovsky, P., Tikk, D. (eds.) Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, 16–20 September 2019, pp. 169–177. ACM (2019)
Liu, Q., Yu, F., Wu, S., Wang, L.: A convolutional click prediction model. In: Bailey, J., et al. (eds.) Proceedings of the 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, VIC, Australia, 19–23 October 2015, pp. 1743–1746. ACM (2015)
Ni, J., Li, J., McAuley, J.J.: Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, 3–7 November 2019, pp. 188–197. Association for Computational Linguistics (2019)
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2009)
Song, W., et al.: Autoint: automatic feature interaction learning via self-attentive neural networks. In: Zhu, W., et al. (eds.) Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, 3–7 November 2019, pp. 1161–1170. ACM (2019)
Wang, H., Zhang, F., Zhao, M., Li, W., Xie, X., Guo, M.: Multi-task feature learning for knowledge graph enhanced recommendation. In: Liu, L., et al. (eds.) The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, 13–17 May 2019, pp. 2000–2010. ACM (2019)
Wang, R., Fu, B., Fu, G., Wang, M.: Deep & cross network for ad click predictions. CoRR abs/1708.05123 (2017)
Wang, W., Feng, F., He, X., Zhang, H., Chua, T.: “Click” is not equal to “like”: Counterfactual recommendation for mitigating clickbait issue. CoRR abs/2009.09945 (2020)
Yen, S.J., Lee, Y.S.: Cluster-based under-sampling approaches for imbalanced data distributions. Expert Syst. Appl. 36(3), 5718–5727 (2009)
Zhou, G., et al.: Deep interest evolution network for click-through rate prediction. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, Honolulu, Hawaii, USA, 27 January - 1 February 2019, pp. 5941–5948. AAAI Press (2019)
Zhou, G., et al.: Deep interest network for click-through rate prediction. CoRR abs/1706.06978 (2017)
Acknowledgements
This research is supported by Science Foundation Ireland through the Insight Centre for Data Analytics.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Q. et al. (2022). MARF: User-Item Mutual Aware Representation with Feedback. In: Di Noia, T., Ko, IY., Schedl, M., Ardito, C. (eds) Web Engineering. ICWE 2022. Lecture Notes in Computer Science, vol 13362. Springer, Cham. https://doi.org/10.1007/978-3-031-09917-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-09917-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09916-8
Online ISBN: 978-3-031-09917-5
eBook Packages: Computer ScienceComputer Science (R0)