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Published in: World Wide Web 4/2021

25-05-2021

Multiple interleaving interests modeling of sequential user behaviors in e-commerce platform

Authors: Yuqiang Han, Qian Li, Yang Xiao, Hucheng Zhou, Zhenglu Yang, Jian Wu

Published in: World Wide Web | Issue 4/2021

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Abstract

An anonymous user-behavior session in an e-commerce platform is a time-stamped series of sequential implicit feedback (e.g., clicks and orders of items) in a short period, without user profiles available (e.g., the non-logged-in users). The accurate modeling of such sessions is crucial for distributed representation learning in user profiling and item embedding. It broadly spans the recommendation scenarios with capabilities, such as next-click item prediction. The statistics of sessions provided by one of the largest e-commerce platforms indicate that a user generally has multiple interests in a session simultaneously, and the interests may be interleaved. Recent advances in recurrent neural networks and attention mechanisms have led to promising approaches for modeling such sessions. However, few of the existing models explicitly consider the effects of multiple interleaving interests. Through specific data analysis, we find there are two characteristics present in those sessions: 1) contiguous items usually share the same user interest(e.g., category), which can measure the intensity of interests in the current window scope (i.e., local features of interests); 2) each interest repeatedly occurs in a session, which shows its importance in the current session (i.e., global features of interests). Based on the observations, we present a novel framework that provides M ultiple I nterleaving I nterests M odeling with the following contributions: 1) a local layer is adopted to extract the local features of interests by the convolution operations; 2) a global layer is utilized to capture the global features of interests in the current sequence by considering the frequency of items; 3) an Interest-GRU layer is adopted to track each interest’s sequential evolution by fusing local and global features. Experimental results of the next-click prediction task on two real-world datasets demonstrate that our proposed method significantly outperforms state-of-the-art models.
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Footnotes
Literature
1.
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al: Tensorflow: A system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, pp. 265–283. USENIX Association, Savannah (2016)
2.
Aggarwal, C.C.: Content-based recommender systems. In: Recommender systems, pp. 139–166. Springer (2016)
3.
Burke, R.: Hybrid recommender systems: Survey and experiments. User Model. User-Adapted Interact. 12(4), 331–370 (2002)CrossRef
4.
Chen, H., Li, Y., Sun, X., dong Xu, G., Yin, H.: Temporal meta-path guided explainable recommendation. arXiv:2101.​01433 (2021)
5.
Chen, T., Yin, H., Chen, H., Yan, R., Nguyen, Q.V.H., Li, X.: Air: Attentional Intention-Aware Recommender Systems. In: 2019 IEEE 35Th International Conference on Data Engineering (ICDE), pp. 304–315. IEEE (2019)
6.
Chen, X., Xu, H., Zhang, Y., Tang, J., Cao, Y., Qin, Z., Zha, H.: Sequential recommendation with user memory networks. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM 2018, pp. 108–116. ACM, Marina Del Rey (2018)
7.
Cui, Q., Wu, S., Liu, Q., Zhong, W., Wang, L.: Mv-rnn: a multi-view recurrent neural network for sequential recommendation. IEEE Trans. Knowl. Data Eng. 32(2), 317–331 (2018)CrossRef
8.
Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Now Publishers Inc (2011)
9.
Gehring, J., Auli, M., Grangier, D., Yarats, D., Dauphin, Y.N.: Convolutional sequence to sequence learning. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, JMLR. org, pp. 1243–1252 (2017)
10.
Gu, W., Dong, S., Zeng, Z.: Increasing recommended effectiveness with markov chains and purchase intervals. Neural Comput. Appl. 25(5), 1153–1162 (2014)CrossRef
11.
Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: DeepFM: a factorization-machine based neural network for ctr prediction. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 1725–1731. ijcai.org, Melbourne (2017)
12.
Hansen, C., Hansen, C., Maystre, L., Mehrotra, R., Brost, B., Tomasi, F., Lalmas, M.: Contextual and sequential user embeddings for large-scale music recommendation. In: RecSys 2020: Fourteenth ACM Conference on Recommender Systems, pp. 53–62. ACM, Virtual Event (2020)
13.
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, pp. 173–182. ACM, Perth (2017)
14.
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, CIKM 2018, pp. 843–852. ACM, Torino (2018)
15.
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: 4th International Conference on Learning Representations, ICLR 2016. Conference Track Proceedings, San Juan (2016)
16.
Hidasi, B., Quadrana, M., Karatzoglou, A., Tikk, D.: Parallel recurrent neural network architectures for feature-rich session-based recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 241–248. ACM, Boston (2016)
17.
Hosseini, S., Yin, H., Zhou, X., Sadiq, S., Kangavari, M.R., Cheung, N.M.: Leveraging multi-aspect time-related influence in location recommendation. World Wide Web 22(3), 1001–1028 (2019)CrossRef
18.
Hou, Y., Yang, N., Wu, Y., Philip, S.Y.: Explainable recommendation with fusion of aspect information. World Wide Web 22(1), 221–240 (2019)CrossRef
19.
Huang, J., Zhao, W.X., Dou, H., Wen, J.R., Chang, E.Y.: Improving sequential recommendation with knowledge-enhanced memory networks. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, pp. 505–514. ACM, Ann Arbor (2018)
20.
Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender systems: an introduction. Cambridge University Press (2010)
21.
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRef
22.
23.
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRef
24.
Lei, M., Chu, L., Wang, Z., Pei, J., He, C., Zhang, X., Fang, B.: Mining top-k sequential patterns in transaction database graphs. World Wide Web 23(1), 103–130 (2020)CrossRef
25.
Li, C., Liu, Z., Wu, M., Xu, Y., Zhao, H., Huang, P., Kang, G., Chen, Q., Li, W., Lee, D.L.: Multi-interest network with dynamic routing for recommendation at tmall. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, pp. 2615–2623. ACM, Beijing (2019)
26.
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J., 2017: Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, pp. 1419–1428. ACM, Singapore
27.
Li, Z., Xie, H., Xu, G., Li, Q., Leng, M., Zhou, C.: Towards purchase prediction: a transaction-based setting and a graph-based method leveraging price information. Pattern Recogn., 107824 (2021)
28.
29.
Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: 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, KDD 2018, pp. 1831–1839. ACM, London (2018)
30.
Lv, F., Jin, T., Yu, C., Sun, F., Lin, Q., Yang, K., Ng, W.: SDM: sequential deep matching model for online large-scale recommender system. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, pp. 2635–2643. ACM, Beijing (2019)
31.
Marcus, M.P., Marcinkiewicz, M.A., Santorini, B.: Building a large annotated corpus of english: The penn treebank. Comput. Linguist. 19(2), 313–330 (1993)
32.
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: The adaptive Web. Springer, pp 325–341 (2007)
33.
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 811–820. ACM, Raleigh (2010)
34.
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the Tenth International World Wide Web Conference, WWW 10, pp. 285–295. ACM, Hong Kong (2001)
35.
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: The adaptive Web. Springer, pp. 291–324 (2007)
36.
Shani, G., Heckerman, D., Brafman, R.I.: An mdp-based recommender system. J. Mach. Learn. Res. 6(Sep), 1265–1295 (2005)MathSciNetMATH
37.
Tan, Y.K., Xu, X., Liu, Y.: Improved recurrent neural networks for session-based recommendations. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, DLRS@RecSys 2016, pp. 17–22. ACM, Boston (2016)
38.
Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM 2018, pp. 565–573. ACM, Marina Del Rey (2018)
39.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, pp. 5998–6008, Long Beach (2017)
40.
Wang, D., Deng, S., Xu, G.: Sequence-based context-aware music recommendation. Inf. Retrieval J. 21(2), 230–252 (2018)CrossRef
41.
Wang, H., Liu, G., Liu, A., Li, Z., Zheng, K.: DMRAN: A hierarchical fine-grained attention-based network for recommendation. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, pp. 3698–3704. AAAI Press, Macao (2019)
42.
Wang, S., Hu, L., Wang, Y., Sheng, Q.Z., Orgun, M., Cao, L.: Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, pp. 3771–3777. AAAI Press, Macao (2019)
43.
Wang, Y., Zhang, C., Wang, S., Philip, S.Y., Bai, L., Cui, L., Xu, G.: Generative temporal link prediction via self-tokenized sequence modeling. World Wide Web 23(4), 2471–2488 (2020)CrossRef
44.
Weston, J., Chopra, S., Bordes, A.: Memory networks. In: 3rd International Conference on Learning Representations, ICLR 2015. Conference Track Proceedings, San Diego (2015)
45.
46.
Wu, C.Y., Ahmed, A., Beutel, A., Smola, A.J., Jing, H.: Recurrent recommender networks. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM 2017, pp. 495–503. ACM, Cambridge (2017)
47.
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, pp. 346–353. AAAI Press, Honolulu (2019)
48.
Yang, N., Ma, Y., Chen, L., Philip, S.Y.: A meta-feature based unified framework for both cold-start and warm-start explainable recommendations. World Wide Web 23(1), 241–265 (2020)CrossRef
49.
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, pp. 974–983. ACM, London (2018)
50.
Yu, F., Liu, Q., Wu, S., Wang, L., Tan, T.: 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, pp. 729–732. ACM, Pisa (2016)
51.
Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J.M., He, X.: A simple convolutional generative network for next item recommendation. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, pp. 582–590. ACM, Melbourne (2019)
52.
Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: A survey and new perspectives. ACM Comput. Surv. (CSUR) 52(1):1–38 (2019)
53.
Zhang, T., Zhao, P., Liu, Y., Sheng, V., Xu, J., Wang, D., Liu, G., Zhou, X.: Feature-level deeper self-attention network for sequential recommendation. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, pp. 4320–4326. AAAI Press, Macao (2019)
54.
Zhao, X., Zhang, L., Ding, Z., Xia, L., Tang, J., Yin, D.: Recommendations with negative feedback via pairwise deep reinforcement learning. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, pp. 1040–1048. ACM, London (2018)
55.
Zhou, C., Sun, C., Liu, Z., Lau, F.: A C-LSTM neural network for text classification. arXiv:1511.​08630 (2015)
56.
Zhou, G., Zhu, X., Song, C., Fan, Y., Zhu, H., Ma, X., Yan, Y., Jin, J., Li, H., Gai, K.: Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, pp. 1059–1068. ACM, London (2018)
57.
Zhou, G., Mou, N., Fan, Y., Pi, Q., Bian, W., Zhou, C., Zhu, X., Gai, K.: Deep Interest Evolution Network for Click-Through Rate Prediction. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, vol. 33, pp. 5941–5948. AAAI Press, Honolulu (2019)
Metadata
Title
Multiple interleaving interests modeling of sequential user behaviors in e-commerce platform
Authors
Yuqiang Han
Qian Li
Yang Xiao
Hucheng Zhou
Zhenglu Yang
Jian Wu
Publication date
25-05-2021
Publisher
Springer US
Published in
World Wide Web / Issue 4/2021
Print ISSN: 1386-145X
Electronic ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-021-00889-0

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