Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
153 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Touch the Core: Exploring Task Dependence Among Hybrid Targets for Recommendation (2403.17442v2)

Published 26 Mar 2024 in cs.IR

Abstract: As user behaviors become complicated on business platforms, online recommendations focus more on how to touch the core conversions, which are highly related to the interests of platforms. These core conversions are usually continuous targets, such as \textit{watch time}, \textit{revenue}, and so on, whose predictions can be enhanced by previous discrete conversion actions. Therefore, multi-task learning (MTL) can be adopted as the paradigm to learn these hybrid targets. However, existing works mainly emphasize investigating the sequential dependence among discrete conversion actions, which neglects the complexity of dependence between discrete conversions and the final continuous conversion. Moreover, simultaneously optimizing hybrid tasks with stronger task dependence will suffer from volatile issues where the core regression task might have a larger influence on other tasks. In this paper, we study the MTL problem with hybrid targets for the first time and propose the model named Hybrid Targets Learning Network (HTLNet) to explore task dependence and enhance optimization. Specifically, we introduce label embedding for each task to explicitly transfer the label information among these tasks, which can effectively explore logical task dependence. We also further design the gradient adjustment regime between the final regression task and other classification tasks to enhance the optimization. Extensive experiments on two offline public datasets and one real-world industrial dataset are conducted to validate the effectiveness of HTLNet. Moreover, online A/B tests on the financial recommender system also show that our model has improved significantly. Our implementation is available here\footnote{\url{https://github.com/fuyuanlyu/HTLNet}}.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (34)
  1. Rich Caruana. 1997. Multitask Learning. Machine Learning 28, 1 (1997), 41–75.
  2. Exploring Logically Dependent Multi-task Learning with Causal Inference. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 2213–2225.
  3. GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018 (Proceedings of Machine Learning Research, Vol. 80), Jennifer G. Dy and Andreas Krause (Eds.). PMLR, 793–802. http://proceedings.mlr.press/v80/chen18a.html
  4. Neural Multi-task Recommendation from Multi-behavior Data. In 35th IEEE International Conference on Data Engineering, ICDE. IEEE, Macao, China, 1554–1557.
  5. KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (Atlanta, GA, USA) (CIKM ’22). 3953–3957.
  6. Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 9). PMLR, Chia Laguna Resort, Sardinia, Italy, 249–256.
  7. MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks. In WWW ’22: The ACM Web Conference 2022. ACM, New York, NY, USA, 2205–2215.
  8. Categorical Reparameterization with Gumbel-Softmax. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net.
  9. 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.).
  10. AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD. ACM, New York, NY, USA, 4370–4379.
  11. RevMan: Revenue-aware Multi-task Online Insurance Recommendation. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI. AAAI Press, Virtual Event, 303–310.
  12. Tree Based Progressive Regression Model for Watch-Time Prediction in Short-Video Recommendation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Long Beach, CA, USA,). Association for Computing Machinery, New York, NY, USA, 4497–4506.
  13. 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, KDD 2018, London, UK, August 19-23, 2018, Yike Guo and Faisal Farooq (Eds.). ACM, 1930–1939.
  14. 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, SIGIR. ACM, New York, NY, USA, 1137–1140.
  15. Cross-Stitch Networks for Multi-Task Learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  16. Mobile App Cross-Domain Recommendation with Multi-Graph Neural Network. ACM Trans. Knowl. Discov. Data 15, 4 (2021), 55:1–55:21. https://doi.org/10.1145/3442201
  17. Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations. In RecSys 2020: Fourteenth ACM Conference on Recommender Systems, Virtual Event, Brazil, September 22-26, 2020, Rodrygo L. T. Santos, Leandro Balby Marinho, Elizabeth M. Daly, Li Chen, Kim Falk, Noam Koenigstein, and Edleno Silva de Moura (Eds.). ACM, 269–278.
  18. Improving Training Stability for Multitask Ranking Models in Recommender Systems. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’23). Association for Computing Machinery, New York, NY, USA, 4882–4893. https://doi.org/10.1145/3580305.3599846
  19. Predicting individual retweet behavior by user similarity: A multi-task learning approach. Knowledge Based System 89 (2015), 681–688.
  20. Task Aware Feature Extraction Framework for Sequential Dependence Multi-Task Learning. In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys. ACM, New York, NY, USA, 151–160.
  21. A deep probabilistic model for customer lifetime value prediction. arXiv preprint arXiv:1912.07753 (2019).
  22. Multi-Task Deep Recommender Systems: A Survey. CoRR abs/2302.03525 (2023). arXiv:2302.03525
  23. 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, SIGIR. ACM, New York, NY, USA, 2377–2386.
  24. Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR. ACM, New York, NY, USA, 1914–1918.
  25. Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, 365–374.
  26. Understanding and Improving Information Transfer in Multi-Task Learning. In 8th International Conference on Learning Representations, ICLR. OpenReview.net, Addis Ababa, Ethiopia.
  27. Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising. In KDD ’21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA, 3745–3755.
  28. Neural Hierarchical Factorization Machines for User’s Event Sequence Analysis. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, China) (SIGIR ’20). Association for Computing Machinery, New York, NY, USA, 1893–1896. https://doi.org/10.1145/3397271.3401307
  29. Learning Reliable User Representations from Volatile and Sparse Data to Accurately Predict Customer Lifetime Value. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (Virtual Event, Singapore) (KDD ’21). Association for Computing Machinery, New York, NY, USA, 3806–3816.
  30. Gradient Surgery for Multi-Task Learning. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 5824–5836.
  31. Deconfounding Duration Bias in Watch-Time Prediction for Video Recommendation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Washington DC, USA) (KDD ’22). Association for Computing Machinery, New York, NY, USA, 4472–4481. https://doi.org/10.1145/3534678.3539092
  32. CTnoCVR: A Novelty Auxiliary Task Making the Lower-CTR-Higher-CVR Upper. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22). Association for Computing Machinery, New York, NY, USA, 2272–2276. https://doi.org/10.1145/3477495.3531843
  33. Deep Learning Based Recommender System: A Survey and New Perspectives. ACM Computing Survey 52, 1 (2019), 5:1–5:38.
  34. DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation. In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE Computer Society, Los Alamitos, CA, USA, 3113–3125.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now