Deep Pattern Network for Click-Through Rate Prediction
July 2024
Pages 1189 - 1199
Abstract
Click-through rate (CTR) prediction plays a pivotal role in real-world applications, particularly in recommendation systems and online advertising. A significant research branch in this domain focuses on user behavior modeling. Current research predominantly centers on modeling co-occurrence relationships between the target item and items previously interacted with by users. However, this focus neglects the intricate modeling of user behavior patterns. In reality, the abundance of user interaction records encompasses diverse behavior patterns, indicative of a spectrum of habitual paradigms. These patterns harbor substantial potential to significantly enhance CTR prediction performance. To harness the informational potential within behavior patterns, we extend Target Attention (TA) to Target Pattern Attention (TPA) to model pattern-level dependencies. Furthermore, three critical challenges demand attention: the inclusion of unrelated items within patterns, data sparsity of patterns, and computational complexity arising from numerous patterns. To address these challenges, we introduce the Deep Pattern Network (DPN), designed to comprehensively leverage information from behavior patterns. DPN efficiently retrieves target-related behavior patterns using a target-aware attention mechanism. Additionally, it contributes to refining patterns through a pre-training paradigm based on self-supervised learning while promoting dependency learning within sparse patterns. Our comprehensive experiments, conducted across three public datasets, substantiate the superior performance and broad compatibility of DPN.
References
[1]
Mart'i n Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, Savannah, GA, USA, November 2--4, 2016, Kimberly Keeton and Timothy Roscoe (Eds.). USENIX Association, 265--283. https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi
[2]
Weijie Bian, Kailun Wu, Lejian Ren, Qi Pi, Yujing Zhang, Can Xiao, Xiang-Rong Sheng, Yong-Nan Zhu, Zhangming Chan, Na Mou, Xinchen Luo, Shiming Xiang, Guorui Zhou, Xiaoqiang Zhu, and Hongbo Deng. 2022. CAN: Feature Co-Action Network for Click-Through Rate Prediction. In WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21 - 25, 2022, K. Selcuk Candan, Huan Liu, Leman Akoglu, Xin Luna Dong, and Jiliang Tang (Eds.). ACM, 57--65. https://doi.org/10.1145/3488560.3498435
[3]
Yue Cao, Xiaojiang Zhou, Jiaqi Feng, Peihao Huang, Yao Xiao, Dayao Chen, and Sheng Chen. 2022. Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17--21, 2022, Mohammad Al Hasan and Li Xiong (Eds.). ACM, 2974--2983. https://doi.org/10.1145/3511808.3557082
[4]
Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, and Chih-Jen Lin. 2010. Training and Testing Low-degree Polynomial Data Mappings via Linear SVM. J. Mach. Learn. Res., Vol. 11 (2010), 1471--1490. https://doi.org/10.5555/1756006.1859899
[5]
Qiwei Chen, Changhua Pei, Shanshan Lv, Chao Li, Junfeng Ge, and Wenwu Ou. 2021. End-to-End User Behavior Retrieval in Click-Through RatePrediction Model. CoRR, Vol. abs/2108.04468 (2021). showeprint[arXiv]2108.04468 https://arxiv.org/abs/2108.04468
[6]
Qiwei Chen, Huan Zhao, Wei Li, Pipei Huang, and Wenwu Ou. 2019. Behavior Sequence Transformer for E-commerce Recommendation in Alibaba. CoRR, Vol. abs/1905.06874 (2019). showeprint[arXiv]1905.06874 http://arxiv.org/abs/1905.06874
[7]
Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018. Sequential Recommendation with User Memory Networks. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM 2018, Marina Del Rey, CA, USA, February 5--9, 2018, Yi Chang, Chengxiang Zhai, Yan Liu, and Yoelle Maarek (Eds.). ACM, 108--116. https://doi.org/10.1145/3159652.3159668
[8]
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. 2016. Wide & Deep Learning for Recommender Systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, DLRS@RecSys 2016, Boston, MA, USA, September 15, 2016, Alexandros Karatzoglou, Balá zs Hidasi, Domonkos Tikk, Oren Sar Shalom, Haggai Roitman, Bracha Shapira, and Lior Rokach (Eds.). ACM, 7--10. https://doi.org/10.1145/2988450.2988454
[9]
Junyoung Chung, cC aglar Gü lcc ehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. CoRR, Vol. abs/1412.3555 (2014). showeprint[arXiv]1412.3555 http://arxiv.org/abs/1412.3555
[10]
Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, September 15--19, 2016, Shilad Sen, Werner Geyer, Jill Freyne, and Pablo Castells (Eds.). ACM, 191--198. https://doi.org/10.1145/2959100.2959190
[11]
Shizhe Diao, Jiaxin Bai, Yan Song, Tong Zhang, and Yonggang Wang. 2020. ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations. In Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16--20 November 2020 (Findings of ACL, Vol. EMNLP 2020). Association for Computational Linguistics, 4729--4740. https://doi.org/10.18653/V1/2020.FINDINGS-EMNLP.425
[12]
Xiaohan Ding, Xiangyu Zhang, Jungong Han, and Guiguang Ding. 2022. Scaling Up Your Kernels to 31texttimes31: Revisiting Large Kernel Design in CNNs. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18--24, 2022. IEEE, 11953--11965. https://doi.org/10.1109/CVPR52688.2022.01166
[13]
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3--7, 2021. OpenReview.net. https://openreview.net/forum?id=YicbFdNTTy
[14]
Francc ois Fleuret. 2004. Fast Binary Feature Selection with Conditional Mutual Information. J. Mach. Learn. Res., Vol. 5 (2004), 1531--1555. http://jmlr.org/papers/volume5/fleuret04a/fleuret04a.pdf
[15]
Alex Graves, Greg Wayne, and Ivo Danihelka. 2014. Neural Turing Machines. CoRR, Vol. abs/1410.5401 (2014). showeprint[arXiv]1410.5401 http://arxiv.org/abs/1410.5401
[16]
Daniel W Greeno, Montrose S Sommers, and Jerome B Kernan. 1973. Personality and implicit behavior patterns. Journal of Marketing Research, Vol. 10, 1 (1973), 63--69.
[17]
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19--25, 2017, Carles Sierra (Ed.). ijcai.org, 1725--1731. https://doi.org/10.24963/ijcai.2017/239
[18]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27--30, 2016. IEEE Computer Society, 770--778. https://doi.org/10.1109/CVPR.2016.90
[19]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, April 3--7, 2017, Rick Barrett, Rick Cummings, Eugene Agichtein, and Evgeniy Gabrilovich (Eds.). ACM, 173--182. https://doi.org/10.1145/3038912.3052569
[20]
Balá zs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based Recommendations with Recurrent Neural Networks. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2--4, 2016, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1511.06939
[21]
Wang-Cheng Kang and Julian J. McAuley. 2018. Self-Attentive Sequential Recommendation. In IEEE International Conference on Data Mining, ICDM 2018, Singapore, November 17--20, 2018. IEEE Computer Society, 197--206. https://doi.org/10.1109/ICDM.2018.00035
[22]
Otto F Kernberg. 2016. What is personality? Journal of personality disorders, Vol. 30, 2 (2016), 145--156.
[23]
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6980
[24]
Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. 2018. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19--23, 2018, Yike Guo and Faisal Farooq (Eds.). ACM, 1754--1763. https://doi.org/10.1145/3219819.3220023
[25]
Weiwen Liu, Wei Guo, Yong Liu, Ruiming Tang, and Hao Wang. 2023. User Behavior Modeling with Deep Learning for Recommendation: Recent Advances. In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, September 18--22, 2023, Jie Zhang, Li Chen, Shlomo Berkovsky, Min Zhang, Tommaso Di Noia, Justin Basilico, Luiz Pizzato, and Yang Song (Eds.). ACM, 1286--1287. https://doi.org/10.1145/3604915.3609496
[26]
H. Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, Sharat Chikkerur, Dan Liu, Martin Wattenberg, Arnar Mar Hrafnkelsson, Tom Boulos, and Jeremy Kubica. 2013. Ad click prediction: a view from the trenches. In The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, August 11--14, 2013, Inderjit S. Dhillon, Yehuda Koren, Rayid Ghani, Ted E. Senator, Paul Bradley, Rajesh Parekh, Jingrui He, Robert L. Grossman, and Ramasamy Uthurusamy (Eds.). ACM, 1222--1230. https://doi.org/10.1145/2487575.2488200
[27]
Chang Meng, Hengyu Zhang, Wei Guo, Huifeng Guo, Haotian Liu, Yingxue Zhang, Hongkun Zheng, Ruiming Tang, Xiu Li, and Rui Zhang. 2023. Hierarchical Projection Enhanced Multi-behavior Recommendation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6--10, 2023. ACM, 4649--4660. https://doi.org/10.1145/3580305.3599838
[28]
Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Da Luo, Kangyi Lin, Junzhou Huang, Sophia Ananiadou, and Peilin Zhao. 2022. Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer. In SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022. ACM, 353--362. https://doi.org/10.1145/3477495.3532031
[29]
Qi Pi, Weijie Bian, Guorui Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4--8, 2019, Ankur Teredesai, Vipin Kumar, Ying Li, Ró mer Rosales, Evimaria Terzi, and George Karypis (Eds.). ACM, 2671--2679. https://doi.org/10.1145/3292500.3330666
[30]
Qi Pi, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, Xiaoqiang Zhu, and Kun Gai. 2020. Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19--23, 2020, Mathieu d'Aquin, Stefan Dietze, Claudia Hauff, Edward Curry, and Philippe Cudré -Mauroux (Eds.). ACM, 2685--2692. https://doi.org/10.1145/3340531.3412744
[31]
Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang. 2016. Product-Based Neural Networks for User Response Prediction. In IEEE 16th International Conference on Data Mining, ICDM 2016, December 12--15, 2016, Barcelona, Spain, Francesco Bonchi, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Zhi-Hua Zhou, and Xindong Wu (Eds.). IEEE Computer Society, 1149--1154. https://doi.org/10.1109/ICDM.2016.0151
[32]
Steffen Rendle. 2010. Factorization Machines. In ICDM 2010, The 10th IEEE International Conference on Data Mining, Sydney, Australia, 14--17 December 2010, Geoffrey I. Webb, Bing Liu, Chengqi Zhang, Dimitrios Gunopulos, and Xindong Wu (Eds.). IEEE Computer Society, 995--1000. https://doi.org/10.1109/ICDM.2010.127
[33]
Aurko Roy, Rohan Anil, Guangda Lai, Benjamin Lee, Jeffrey Zhao, Shuyuan Zhang, Shibo Wang, Ye Zhang, Shen Wu, Rigel Swavely, Tao Yu, Phuong Dao, Christopher Fifty, Zhifeng Chen, and Yonghui Wu. 2022. N-Grammer: Augmenting Transformers with latent n-grams. CoRR, Vol. abs/2207.06366 (2022). https://doi.org/10.48550/ARXIV.2207.06366 showeprint[arXiv]2207.06366
[34]
Alexander Shishkin, Anastasia A. Bezzubtseva, Alexey Drutsa, Ilia Shishkov, Ekaterina Gladkikh, Gleb Gusev, and Pavel Serdyukov. 2016. Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5--10, 2016, Barcelona, Spain, Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett (Eds.). 4637--4645. https://proceedings.neurips.cc/paper/2016/hash/d5e2fbef30a4eb668a203060ec8e5eef-Abstract.html
[35]
Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1409.1556
[36]
Gustavo Sosa-Cabrera, Miguel García-Torres, Santiago Gómez-Guerrero, Christian E. Schaerer, and Federico Divina. 2019. A multivariate approach to the symmetrical uncertainty measure: Application to feature selection problem. Information Sciences, Vol. 494 (2019), 1--20. https://doi.org/10.1016/j.ins.2019.04.046
[37]
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3--7, 2019, Wenwu Zhu, Dacheng Tao, Xueqi Cheng, Peng Cui, Elke A. Rundensteiner, David Carmel, Qi He, and Jeffrey Xu Yu (Eds.). ACM, 1441--1450. https://doi.org/10.1145/3357384.3357895
[38]
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to Sequence Learning with Neural Networks. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8--13 2014, Montreal, Quebec, Canada, Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, and Kilian Q. Weinberger (Eds.). 3104--3112. https://proceedings.neurips.cc/paper/2014/hash/a14ac55a4f27472c5d894ec1c3c743d2-Abstract.html
[39]
Jiaxi Tang and Ke Wang. 2018. 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, Marina Del Rey, CA, USA, February 5--9, 2018, Yi Chang, Chengxiang Zhai, Yan Liu, and Yoelle Maarek (Eds.). ACM, 565--573. https://doi.org/10.1145/3159652.3159656
[40]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4--9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 5998--6008. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
[41]
Wen Wang, Wei Zhang, Shukai Liu, Qi Liu, Bo Zhang, Leyu Lin, and Hongyuan Zha. 2020. Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction. In WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20--24, 2020. ACM / IW3C2, 3056--3062. https://doi.org/10.1145/3366423.3380077
[42]
Satosi Watanabe. 1960. Information theoretical analysis of multivariate correlation. IBM Journal of research and development, Vol. 4, 1 (1960), 66--82.
[43]
Ling Yan, Wu-Jun Li, Gui-Rong Xue, and Dingyi Han. 2014. Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising. In Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21--26 June 2014 (JMLR Workshop and Conference Proceedings, Vol. 32). JMLR.org, 802--810. http://proceedings.mlr.press/v32/yan14.html
[44]
Yi Yang, Baile Xu, Shaofeng Shen, Furao Shen, and Jian Zhao. 2020. Operation-aware Neural Networks for user response prediction. Neural Networks, Vol. 121 (2020), 161--168. https://doi.org/10.1016/j.neunet.2019.09.020
[45]
Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhaojie Gong, Fangda Gu, Michael He, Yinghai Lu, and Yu Shi. 2024. Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations. CoRR, Vol. abs/2402.17152 (2024). https://doi.org/10.48550/ARXIV.2402.17152 showeprint[arXiv]2402.17152
[46]
Hengyu Zhang, Enming Yuan, Wei Guo, Zhicheng He, Jiarui Qin, Huifeng Guo, Bo Chen, Xiu Li, and Ruiming Tang. 2022. Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17--21, 2022. ACM, 2549--2558. https://doi.org/10.1145/3511808.3557289
[47]
Weinan Zhang, Jiarui Qin, Wei Guo, Ruiming Tang, and Xiuqiang He. 2021. Deep Learning for Click-Through Rate Estimation. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19--27 August 2021, Zhi-Hua Zhou (Ed.). ijcai.org, 4695--4703. https://doi.org/10.24963/ijcai.2021/636
[48]
Zuowu Zheng, Xiaofeng Gao, Junwei Pan, Qi Luo, Guihai Chen, Dapeng Liu, and Jie Jiang. 2022. Autoattention: automatic field pair selection for attention in user behavior modeling. In 2022 IEEE International Conference on Data Mining (ICDM). IEEE, 803--812.
[49]
Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Deep Interest Evolution Network for Click-Through Rate Prediction. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019. AAAI Press, 5941--5948. https://doi.org/10.1609/aaai.v33i01.33015941
[50]
Guorui Zhou, Xiaoqiang Zhu, Chengru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep Interest Network for Click-Through Rate Prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19--23, 2018, Yike Guo and Faisal Farooq (Eds.). ACM, 1059--1068. https://doi.org/10.1145/3219819.3219823
[51]
Haolin Zhou, Junwei Pan, Xinyi Zhou, Xihua Chen, Jie Jiang, Xiaofeng Gao, and Guihai Chen. 2023. Temporal Interest Network for Click-Through Rate Prediction. CoRR, Vol. abs/2308.08487 (2023).
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July 2024
3164 pages
ISBN:9798400704314
DOI:10.1145/3626772
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