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Utilizing Non-click Samples via Semi-supervised Learning for Conversion Rate Prediction

Published: 08 October 2024 Publication History

Abstract

Conversion rate (CVR) prediction is essential in recommender systems, facilitating precise matching between recommended items and users’ preferences. However, the sample selection bias (SSB) and data sparsity (DS) issues pose challenges to accurate prediction. Existing works have proposed the click-through and conversion rate (CTCVR) prediction task which models samples from exposure to ``click and conversion" in entire space and incorporates multi-task learning. This approach has shown efficacy in mitigating these challenges. Nevertheless, it intensifies the false negative sample (FNS) problem. To be more specific, the CTCVR task implicitly treats all the CVR labels of non-click samples as negative, overlooking the possibility that some samples might convert if clicked. This oversight can negatively impact CVR model performance, as empirical analysis has confirmed. To this end, we advocate for discarding the CTCVR task and proposing a Non-click samples Improved Semi-supErvised (NISE) method for conversion rate prediction, where the non-click samples are treated as unlabeled. Our approach aims to predict their probabilities of conversion if clicked, utilizing these predictions as pseudo-labels for further model training. This strategy can help alleviate the FNS problem, and direct modeling of the CVR task across the entire space also mitigates the SSB and DS challenges. Additionally, we conduct multi-task learning by introducing an auxiliary click-through rate prediction task, thereby enhancing embedding layer representations. Our approach is applicable to various multi-task architectures. Comprehensive experiments are conducted on both public and production datasets, demonstrating the superiority of our proposed method in mitigating the FNS challenge and improving the CVR estimation. The implementation code is available at https://github.com/Hjh233/NISE.

References

[1]
Deepak Agarwal, Rahul Agrawal, Rajiv Khanna, and Nagaraj Kota. 2010. Estimating rates of rare events with multiple hierarchies through scalable log-linear models. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. 213–222.
[2]
Rich Caruana. 1997. Multitask learning. Machine learning 28 (1997), 41–75.
[3]
Olivier Chapelle. 2014. Modeling delayed feedback in display advertising. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 1097–1105.
[4]
Chong Chen, Weizhi Ma, Min Zhang, Chenyang Wang, Yiqun Liu, and Shaoping Ma. 2023. Revisiting negative sampling vs. non-sampling in implicit recommendation. ACM Transactions on Information Systems 41, 1 (2023), 1–25.
[5]
Chong Chen, Min Zhang, Chenyang Wang, Weizhi Ma, Minming Li, Yiqun Liu, and Shaoping Ma. 2019. An efficient adaptive transfer neural network for social-aware recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 225–234.
[6]
Chong Chen, Min Zhang, Yongfeng Zhang, Yiqun Liu, and Shaoping Ma. 2020. Efficient neural matrix factorization without sampling for recommendation. ACM Transactions on Information Systems (TOIS) 38, 2 (2020), 1–28.
[7]
Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin, and Keping Yang. 2021. AutoDebias: Learning to debias for recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 21–30.
[8]
Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023. Bias and debias in recommender system: A survey and future directions. ACM Transactions on Information Systems 41, 3 (2023), 1–39.
[9]
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich. 2018. Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks. In International conference on machine learning. PMLR, 794–803.
[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. 191–198.
[11]
Quanyu Dai, Haoxuan Li, Peng Wu, Zhenhua Dong, Xiao-Hua Zhou, Rui Zhang, Rui Zhang, and Jie Sun. 2022. A generalized doubly robust learning framework for debiasing post-click conversion rate prediction. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 252–262.
[12]
Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, and Depeng Jin. 2020. Simplify and robustify negative sampling for implicit collaborative filtering. Advances in Neural Information Processing Systems 33 (2020), 1094–1105.
[13]
Chongming Gao, Shijun Li, Yuan Zhang, Jiawei Chen, Biao Li, Wenqiang Lei, Peng Jiang, and Xiangnan He. 2022. KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 3953–3957.
[14]
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017).
[15]
Huifeng Guo, Jinkai Yu, Qing Liu, Ruiming Tang, and Yuzhou Zhang. 2019. PAL: a position-bias aware learning framework for CTR prediction in live recommender systems. In Proceedings of the 13th ACM Conference on Recommender Systems. 452–456.
[16]
Michelle Guo, Albert Haque, De-An Huang, Serena Yeung, and Li Fei-Fei. 2018. Dynamic task prioritization for multitask learning. In Proceedings of the European conference on computer vision (ECCV). 270–287.
[17]
Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. 2016. Fast matrix factorization for online recommendation with implicit feedback. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. 549–558.
[18]
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE international conference on data mining. Ieee, 263–272.
[19]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
[20]
Shikun Liu, Edward Johns, and Andrew J Davison. 2019. End-to-end multi-task learning with attention. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 1871–1880.
[21]
Xiaoyang Liu, Chong Liu, Pinzheng Wang, Rongqin Zheng, Lixin Zhang, Leyu Lin, Zhijun Chen, and Liangliang Fu. 2023. UFNRec: Utilizing False Negative Samples for Sequential Recommendation. In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM). SIAM, 46–54.
[22]
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018. Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1930–1939.
[23]
Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, and Kun Gai. 2018. Entire space multi-task model: An effective approach for estimating post-click conversion rate. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 1137–1140.
[24]
Benjamin Marlin, Richard S Zemel, Sam Roweis, and Malcolm Slaney. 2012. Collaborative filtering and the missing at random assumption. arXiv preprint arXiv:1206.5267 (2012).
[25]
H Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, 2013. Ad click prediction: a view from the trenches. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 1222–1230.
[26]
Yabo Ni, Dan Ou, Shichen Liu, Xiang Li, Wenwu Ou, Anxiang Zeng, and Luo Si. 2018. Perceive your users in depth: Learning universal user representations from multiple e-commerce tasks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 596–605.
[27]
Steffen Rendle and Christoph Freudenthaler. 2014. Improving pairwise learning for item recommendation from implicit feedback. In Proceedings of the 7th ACM international conference on Web search and data mining. 273–282.
[28]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).
[29]
Matthew Richardson, Ewa Dominowska, and Robert Ragno. 2007. Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16th international conference on World Wide Web. 521–530.
[30]
Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020. Unbiased recommender learning from missing-not-at-random implicit feedback. In Proceedings of the 13th International Conference on Web Search and Data Mining. 501–509.
[31]
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. In international conference on machine learning. PMLR, 1670–1679.
[32]
Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. 2020. Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations. In Proceedings of the 14th ACM Conference on Recommender Systems. 269–278.
[33]
Hao Wang, Tai-Wei Chang, Tianqiao Liu, Jianmin Huang, Zhichao Chen, Chao Yu, Ruopeng Li, and Wei Chu. 2022. Escm2: Entire space counterfactual multi-task model for post-click conversion rate estimation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 363–372.
[34]
Ruoxi Wang, Rakesh Shivanna, Derek Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed Chi. 2021. Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems. In Proceedings of the web conference 2021. 1785–1797.
[35]
Xuanhui Wang, Michael Bendersky, Donald Metzler, and Marc Najork. 2016. Learning to rank with selection bias in personal search. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. 115–124.
[36]
Xiaojie Wang, Rui Zhang, Yu Sun, and Jianzhong Qi. 2019. Doubly robust joint learning for recommendation on data missing not at random. In International Conference on Machine Learning. PMLR, 6638–6647.
[37]
Xiaojie Wang, Rui Zhang, Yu Sun, and Jianzhong Qi. 2021. Combating selection biases in recommender systems with a few unbiased ratings. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 427–435.
[38]
Yifan Wang, Peijie Sun, Min Zhang, Qinglin Jia, Jingjie Li, and Shaoping Ma. 2023. Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2456–2466.
[39]
Hong Wen, Jing Zhang, Yuan Wang, Fuyu Lv, Wentian Bao, Quan Lin, and Keping Yang. 2020. Entire space multi-task modeling via post-click behavior decomposition for conversion rate prediction. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 2377–2386.
[40]
Wenhao Zhang, Wentian Bao, Xiao-Yang Liu, Keping Yang, Quan Lin, Hong Wen, and Ramin Ramezani. 2020. Large-scale causal approaches to debiasing post-click conversion rate estimation with multi-task learning. In Proceedings of The Web Conference 2020. 2775–2781.
[41]
Weinan Zhang, Tianqi Chen, Jun Wang, and Yong Yu. 2013. Optimizing top-n collaborative filtering via dynamic negative item sampling. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. 785–788.
[42]
Guorui Zhou, Xiaoqiang Zhu, Chenru 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. 1059–1068.
[43]
Feng Zhu, Mingjie Zhong, Xinxing Yang, Longfei Li, Lu Yu, Tiehua Zhang, Jun Zhou, Chaochao Chen, Fei Wu, Guanfeng Liu, 2023. DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation. arXiv preprint arXiv:2302.06141 (2023).

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cover image ACM Conferences
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
October 2024
1438 pages
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Published: 08 October 2024

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Author Tags

  1. Conversion Rate Prediction
  2. False Negative Samples
  3. Recommender Systems
  4. Semi-supervised Learning

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