Multi-view improved sequence behavior with adaptive multi-task learning in ranking
Author:
Funder
the Science and Technology Innovation 2030-New Generation Artificial Intelligence major project
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-022-04088-w.pdf
Reference39 articles.
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2. Zhou G, Mou N, Fan Y, Pi Q, Bian W, Zhou C, Gai K (2019) Deep interest evolution network for click-through rate prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol 33(01), pp 5941–5948
3. Chen Q, Zhao H, Li W, Huang P, Ou W (2019) Behavior sequence transformer for e-commerce recommendation in alibaba. In: Proceedings of the 1st international workshop on deep learning practice for high-dimensional sparse data, pp 1–4
4. Xiao Z, Yang L, Jiang W, Wei Y, Hu Y, Wang H (2020) Deep multi-interest network for click-through rate prediction. In: Proceedings of the 29th ACM international conference on information and knowledge management, pp 2265–2268
5. Xu W, He H, Tan M, Li Y, Lang J, Guo D (2020) 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, pp 1905–1908
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