Abstract
Sequence recommendation is one of the hotspots of recommendation algorithm research. Most of the existing sequence recommendation methods focus on how to use the items’ attributes to characterize the user’s preferences, ignoring that the user behavior also can reflect the preference for items. However, user behavior often has problems of mis-interaction and random interaction, which leads to fully utilizing it difficultly. Therefore, this paper proposes a new Behavior-Item based Hybrid Intent-aware Framework (BIHIF). In this framework, the user’s main intent is extracted based on user behaviors and interactive items, respectively, the two intent vectors are combined and extracted by the full connection layer to obtain the user’s real intent. We use real intent and item vector to calculate the score of the candidate items and make Top-K recommendations. Based on the framework, we implement models respectively by MLP and GRU, which show good results in the experiments based on three real-world datasets.
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References
Loyola, P., Liu, C., Hirate, Y.: Modeling user session and intent with an attention-based encoder-decoder architecture. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 147–151. ACM (2017)
Zheng, L., Lu, C.T., He, L., et al.: Mars: memory attention-aware recommender system. arXiv preprint arXiv:1805.07037 (2018)
Bai, T., Du, P., Zhao, W.X., et al.: A long-short demands-aware model for next-item recommendation. arXiv preprint arXiv:1903.00066 (2019)
Wang, S., Cao, L., Wang, Y.: A survey on session-based recommender systems. arXiv preprint arXiv:1902.04864 (2019)
Zhang, S., Tay, Y., Yao, L., et al.: Next item recommendation with self-attentive metric learning. In: Thirty-Third AAAI Conference on Artificial Intelligence, November 2019
Fang, H., Guo, G., Zhang, D., et al.: Deep learning-based sequential recommender systems: concepts, algorithms, and evaluations. In: International Conference on Web Engineering, pp. 574–577. Springer, Cham (2019)
He, X., Zhang, H., Kan, M.-Y., Chua, T.-S.: Fastmatrix factorization for online recommendation with implicit feedback. In: Proceedings of ACM SIGIR 2016. ACM, Pisa, Italy, pp. 549–558 (2016)
Wasf, A.M.A.: Collecting user access patterns for building user profiles and collaborative filtering. In: Proceedings of the 4th International Conference on Intelligent User Interfaces, pp. 57–64. ACM (1998)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Proceedings of NIPS 2014, 08–13 December, pp. 3104–3112. MIT Press, Montreal (2014)
Balázs, H., Quadrana, M., Karatzoglou, A., Tikk, D.: Parallel recurrent neural network architectures for feature-rich session-based recommendations. In: Proceedings of ACM RecSys 2016, pp. 241–248. ACM, Boston (2016)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: Proceedings of ICLR 2015, 2–4 May. CoRR, San Juan, Puerto Rico (2015)
Hu, L., Cao, L., Wang, S., Xu, G., Cao, J., Gu, Z.: Diversifying personalized recommendation with user-session context. In: Proceedings of IJCAI 2017, IJCAI, Melbourne, Australia, pp. 1858–1864 (2017)
Li, J., Ren, P., Chen, Z., Ren, Z., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of ACM CIKM 2017, Singapore, Singapore, pp. 1419–1428 (2017)
Tan, Y.K., Xu, X., Liu, Y.: Improved recurrent neural networks for session-based recommendations. In: Proceedings of DLRS 2016, 15 September, pp. 17–22. ACM, Boston (2016)
Yu, F., Liu, Q., Wu, S., Wang, L., Tan, T.: A dynamic recurrent model for next basket recommendation. In: Proceedings of ACM SIGIR’16, 17–21 July, pp. 729–732. ACM, Pisa (2016)
Liu, Q., Zeng, Y., Mokhosi, R., et al.: STAMP: short-term attention/memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1831–1839. ACM (2018)
Kang, W.C., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 197–206. IEEE (2018)
He, X., Liao, L., Zhang, H., et al.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, pp. 173–182 (2017)
Hidasi, B., Karatzoglou, A.: Recurrent neural networks with top-k gains for session-based recommendations. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 843–852. ACM (2018)
Acknowledgments
This work was supported by National Key Research and Development Program of China (2018YFB1004500), National Natural Science Foundation of China (61877048), Innovative Research Group of the National Natural Science Foundation of China (61721002), Innovation Research Team of Ministry of Education (IRT_17R86), Project of China Knowledge Centre for Engineering Science and Technology, the Natural Science Basic Research Plan in Shaanxi Province of China under Grant No. 2019JM-458.
We thank the anonymous reviewers for taking time to read and make valuable comments on this paper.
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Chen, Y. et al. (2020). A Behavior-Item Based Hybrid Intention-Aware Frame for Sequence Recommendation. In: Chao, KM., Jiang, L., Hussain, O., Ma, SP., Fei, X. (eds) Advances in E-Business Engineering for Ubiquitous Computing. ICEBE 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-030-34986-8_42
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DOI: https://doi.org/10.1007/978-3-030-34986-8_42
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