Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
175 tokens/sec
GPT-4o
8 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Modeling User Fatigue for Sequential Recommendation (2405.11764v2)

Published 20 May 2024 in cs.IR

Abstract: Recommender systems filter out information that meets user interests. However, users may be tired of the recommendations that are too similar to the content they have been exposed to in a short historical period, which is the so-called user fatigue. Despite the significance for a better user experience, user fatigue is seldom explored by existing recommenders. In fact, there are three main challenges to be addressed for modeling user fatigue, including what features support it, how it influences user interests, and how its explicit signals are obtained. In this paper, we propose to model user Fatigue in interest learning for sequential Recommendations (FRec). To address the first challenge, based on a multi-interest framework, we connect the target item with historical items and construct an interest-aware similarity matrix as features to support fatigue modeling. Regarding the second challenge, built upon feature cross, we propose a fatigue-enhanced multi-interest fusion to capture long-term interest. In addition, we develop a fatigue-gated recurrent unit for short-term interest learning, with temporal fatigue representations as important inputs for constructing update and reset gates. For the last challenge, we propose a novel sequence augmentation to obtain explicit fatigue signals for contrastive learning. We conduct extensive experiments on real-world datasets, including two public datasets and one large-scale industrial dataset. Experimental results show that FRec can improve AUC and GAUC up to 0.026 and 0.019 compared with state-of-the-art models, respectively. Moreover, large-scale online experiments demonstrate the effectiveness of FRec for fatigue reduction. Our codes are released at https://github.com/tsinghua-fib-lab/SIGIR24-FRec.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (45)
  1. Soft frequency capping for improved ad click prediction in yahoo gemini native. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2793–2801.
  2. Survey on the objectives of recommender systems: Measures, solutions, evaluation methodology, and new perspectives. Comput. Surveys 55, 5 (2022), 1–38.
  3. Neural news recommendation with long-and short-term user representations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 336–345.
  4. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018).
  5. Controllable multi-interest framework for recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2942–2951.
  6. Sequential Recommendation with Graph Neural Networks. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 378–387.
  7. Intent contrastive learning for sequential recommendation. In Proceedings of the ACM Web Conference 2022. 2172–2182.
  8. Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems. 7–10.
  9. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014).
  10. Reinforced Negative Sampling for Recommendation with Exposure Data.. In IJCAI. Macao, 2230–2236.
  11. Simplify and robustify negative sampling for implicit collaborative filtering. Advances in Neural Information Processing Systems 33 (2020), 1094–1105.
  12. Deep learning models for serendipity recommendations: a survey and new perspectives. Comput. Surveys 56, 1 (2023), 1–26.
  13. Causal inference in recommender systems: A survey and future directions. ACM Transactions on Information Systems 42, 4 (2024), 1–32.
  14. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017).
  15. Inductive representation learning on large graphs. Advances in neural information processing systems 30 (2017).
  16. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).
  17. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.
  18. AdaMCT: adaptive mixture of CNN-transformer for sequential recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 976–986.
  19. Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 197–206.
  20. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
  21. Multi-interest network with dynamic routing for recommendation at Tmall. In Proceedings of the 28th ACM international conference on information and knowledge management. 2615–2623.
  22. Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 1419–1428.
  23. Time interval aware self-attention for sequential recommendation. In Proceedings of the 13th international conference on web search and data mining. 322–330.
  24. FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation. In Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part IV. Springer, 502–514.
  25. Multi-Intention Oriented Contrastive Learning for Sequential Recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 411–419.
  26. Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations. arXiv preprint arXiv:2102.09211 (2021).
  27. Dual contrastive network for sequential recommendation. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval. 2686–2691.
  28. A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130 (2017).
  29. User fatigue in online news recommendation. In Proceedings of the 25th International Conference on World Wide Web. 1363–1372.
  30. Fatigue-Aware Ad Creative Selection. arXiv preprint arXiv:1908.08936 (2019).
  31. 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. 2671–2679.
  32. Alleviating Video-length Effect for Micro-video Recommendation. ACM Transactions on Information Systems 42, 2 (2023), 1–24.
  33. Robust preference-guided denoising for graph based social recommendation. In Proceedings of the ACM Web Conference 2023. 1097–1108.
  34. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1441–1450.
  35. 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. 565–573.
  36. When multi-level meets multi-interest: A multi-grained neural model for sequential recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1632–1641.
  37. Deep & cross network for ad click predictions. In Proceedings of the ADKDD’17. 1–7.
  38. Multi-granularity Fatigue in Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 4595–4599.
  39. Recurrent convolutional neural network for sequential recommendation. In The world wide web conference. 3398–3404.
  40. CosRec: 2D convolutional neural networks for sequential recommendation. In Proceedings of the 28th ACM international conference on information and knowledge management. 2173–2176.
  41. Self-supervised learning for recommender systems: A survey. arXiv preprint arXiv:2203.15876 (2022).
  42. Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation.. In IJCAI. 4213–4219.
  43. Disentangling long and short-term interests for recommendation. In Proceedings of the ACM Web Conference 2022. 2256–2267.
  44. Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 5941–5948.
  45. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1059–1068.

Summary

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

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

Tweets