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AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction (2312.06683v2)

Published 9 Dec 2023 in cs.IR

Abstract: Click-through rate (CTR) prediction is a vital task in industrial recommendation systems. Most existing methods focus on the network architecture design of the CTR model for better accuracy and suffer from the data sparsity problem. Especially in industrial recommendation systems, the widely applied negative sample down-sampling technique due to resource limitation worsens the problem, resulting in a decline in performance. In this paper, we propose \textbf{A}uxiliary Match \textbf{T}asks for enhancing \textbf{C}lick-\textbf{T}hrough \textbf{R}ate prediction accuracy (AT4CTR) by alleviating the data sparsity problem. Specifically, we design two match tasks inspired by collaborative filtering to enhance the relevance modeling between user and item. As the "click" action is a strong signal which indicates the user's preference towards the item directly, we make the first match task aim at pulling closer the representation between the user and the item regarding the positive samples. Since the user's past click behaviors can also be treated as the user him/herself, we apply the next item prediction as the second match task. For both the match tasks, we choose the InfoNCE as their loss function. The two match tasks can provide meaningful training signals to speed up the model's convergence and alleviate the data sparsity. We conduct extensive experiments on one public dataset and one large-scale industrial recommendation dataset. The result demonstrates the effectiveness of the proposed auxiliary match tasks. AT4CTR has been deployed in the real industrial advertising system and has gained remarkable revenue.

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References (32)
  1. CAN: feature co-action network for click-through rate prediction. In Proceedings of the fifteenth ACM international conference on web search and data mining, 57–65.
  2. TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou. arXiv preprint arXiv:2302.02352.
  3. A simple framework for contrastive learning of visual representations. In International conference on machine learning, 1597–1607. PMLR.
  4. Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems, 7–10.
  5. A Deep Behavior Path Matching Network for Click-Through Rate Prediction. arXiv preprint arXiv:2302.00302.
  6. Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821.
  7. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247.
  8. Buying or browsing?: Predicting real-time purchasing intent using attention-based deep network with multiple behavior. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 1984–1992.
  9. Miss: Multi-interest self-supervised learning framework for click-through rate prediction. In 2022 IEEE 38th international conference on data engineering (ICDE), 727–740. IEEE.
  10. Practical lessons from predicting clicks on ads at facebook. In Proceedings of the eighth international workshop on data mining for online advertising, 1–9.
  11. xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, 1754–1763.
  12. MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1384–1395.
  13. Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 3312–3321.
  14. Deep match to rank model for personalized click-through rate prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, 156–163.
  15. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26.
  16. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748.
  17. Representation learning-assisted click-through rate prediction. arXiv preprint arXiv:1906.04365.
  18. Search-based user interest modeling with lifelong sequential behavior data for click-through rate prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2685–2692.
  19. Rendle, S. 2010. Factorization machines. In 2010 IEEE International conference on data mining, 995–1000. IEEE.
  20. Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16th international conference on World Wide Web, 521–530.
  21. Attention is all you need. Advances in neural information processing systems, 30.
  22. Understanding the behaviour of contrastive loss. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2495–2504.
  23. CL4CTR: A Contrastive Learning Framework for CTR Prediction. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 805–813.
  24. Deep & cross network for ad click predictions. In Proceedings of the ADKDD’17, 1–7.
  25. 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.
  26. Contrastive learning for sequential recommendation. In 2022 IEEE 38th international conference on data engineering (ICDE), 1259–1273. IEEE.
  27. FairLISA: Fair User Modeling with Limited Sensitive Attributes Information. In Thirty-seventh Conference on Neural Information Processing Systems.
  28. Atrank: An attention-based user behavior modeling framework for recommendation. In Proceedings of the AAAI conference on artificial intelligence, volume 32.
  29. Contrastive learning for debiased candidate generation in large-scale recommender systems. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 3985–3995.
  30. Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI conference on artificial intelligence, volume 33, 5941–5948.
  31. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, 1059–1068.
  32. Learning tree-based deep model for recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1079–1088.
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Authors (7)
  1. Qi Liu (485 papers)
  2. Xuyang Hou (4 papers)
  3. Defu Lian (142 papers)
  4. Zhe Wang (574 papers)
  5. Haoran Jin (5 papers)
  6. Jia Cheng (20 papers)
  7. Jun Lei (13 papers)
Citations (5)
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