Papers
Topics
Authors
Recent
2000 character limit reached

MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation (2403.06747v5)

Published 11 Mar 2024 in cs.IR

Abstract: Compared to business-to-consumer (B2C) e-commerce systems, consumer-to-consumer (C2C) e-commerce platforms usually encounter the limited-stock problem, that is, a product can only be sold one time in a C2C system. This poses several unique challenges for click-through rate (CTR) prediction. Due to limited user interactions for each product (i.e. item), the corresponding item embedding in the CTR model may not easily converge. This makes the conventional sequence modeling based approaches cannot effectively utilize user history information since historical user behaviors contain a mixture of items with different volume of stocks. Particularly, the attention mechanism in a sequence model tends to assign higher score to products with more accumulated user interactions, making limited-stock products being ignored and contribute less to the final output. To this end, we propose the Meta-Split Network (MSNet) to split user history sequence regarding to the volume of stock for each product, and adopt differentiated modeling approaches for different sequences. As for the limited-stock products, a meta-learning approach is applied to address the problem of inconvergence, which is achieved by designing meta scaling and shifting networks with ID and side information. In addition, traditional approach can hardly update item embedding once the product is consumed. Thereby, we propose an auxiliary loss that makes the parameters updatable even when the product is no longer in distribution. To the best of our knowledge, this is the first solution addressing the recommendation of limited-stock product. Experimental results on the production dataset and online A/B testing demonstrate the effectiveness of our proposed method.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (44)
  1. GIFT: Graph-GuIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (Atlanta, GA, USA) (CIKM ’22). Association for Computing Machinery, New York, NY, USA, 2964–2973. https://doi.org/10.1145/3511808.3557120
  2. Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Long Beach, CA, USA) (KDD ’23). Association for Computing Machinery, New York, NY, USA, 3774–3784. https://doi.org/10.1145/3580305.3599788
  3. PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information. arXiv:2302.01115 [cs.IR]
  4. Behavior Sequence Transformer for E-Commerce Recommendation in Alibaba. In Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data (Anchorage, Alaska) (DLP-KDD ’19). Association for Computing Machinery, New York, NY, USA, Article 12, 4 pages. https://doi.org/10.1145/3326937.3341261
  5. Wide & Deep Learning for Recommender Systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (Boston, MA, USA) (DLRS 2016). Association for Computing Machinery, New York, NY, USA, 7–10. https://doi.org/10.1145/2988450.2988454
  6. Deep Neural Networks for YouTube Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (Boston, Massachusetts, USA) (RecSys ’16). Association for Computing Machinery, New York, NY, USA, 191–198. https://doi.org/10.1145/2959100.2959190
  7. Class-Balanced Loss Based on Effective Number of Samples. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 9260–9269. https://doi.org/10.1109/CVPR.2019.00949
  8. M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems. arXiv:2205.08084 [cs.IR]
  9. POSO: Personalized Cold Start Modules for Large-scale Recommender Systems. arXiv:2108.04690 [cs.IR]
  10. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. (2017).
  11. Jerome H. Friedman. 2001. Greedy function approximation: A gradient boosting machine. Annals of Statistics 29 (2001), 1189–1232. https://api.semanticscholar.org/CorpusID:39450643
  12. Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (¡conf-loc¿, ¡city¿Birmingham¡/city¿, ¡country¿United Kingdom¡/country¿, ¡/conf-loc¿) (CIKM ’23). Association for Computing Machinery, New York, NY, USA, 4574–4580. https://doi.org/10.1145/3583780.3615496
  13. Mihajlo Grbovic and Haibin Cheng. 2018. Real-Time Personalization Using Embeddings for Search Ranking at Airbnb. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (London, United Kingdom) (KDD ’18). Association for Computing Machinery, New York, NY, USA, 311–320. https://doi.org/10.1145/3219819.3219885
  14. Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction. arXiv preprint arXiv:2312.08727 (2023).
  15. DeepFM: A Factorization-Machine Based Neural Network for CTR Prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (Melbourne, Australia) (IJCAI’17). AAAI Press, 1725–1731.
  16. PS-SA: An Efficient Self-Attention via Progressive Sampling for User Behavior Sequence Modeling. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (¡conf-loc¿, ¡city¿Birmingham¡/city¿, ¡country¿United Kingdom¡/country¿, ¡/conf-loc¿) (CIKM ’23). Association for Computing Machinery, New York, NY, USA, 4639–4645. https://doi.org/10.1145/3583780.3615495
  17. GateNet: gating-enhanced deep network for click-through rate prediction. arXiv preprint arXiv:2007.03519 (2020).
  18. FiBiNET: Combining Feature Importance and Bilinear Feature Interaction for Click-through Rate Prediction. In Proceedings of the 13th ACM Conference on Recommender Systems (Copenhagen, Denmark) (RecSys ’19). Association for Computing Machinery, New York, NY, USA, 169–177. https://doi.org/10.1145/3298689.3347043
  19. Matrix Factorization Techniques for Recommender Systems. Computer 42, 8 (2009), 30–37. https://doi.org/10.1109/MC.2009.263
  20. 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 (London, United Kingdom) (KDD ’18). Association for Computing Machinery, New York, NY, USA, 1930–1939. https://doi.org/10.1145/3219819.3220007
  21. Cross-Domain Recommendation: An Embedding and Mapping Approach. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (Melbourne, Australia) (IJCAI’17). AAAI Press, 2464–2470.
  22. Long-tail learning via logit adjustment. In International Conference on Learning Representations. https://openreview.net/forum?id=37nvvqkCo5
  23. Image Feature Learning for Cold Start Problem in Display Advertising. In Proceedings of the 24th International Conference on Artificial Intelligence (Buenos Aires, Argentina) (IJCAI’15). AAAI Press, 3728–3734.
  24. Warm Up Cold-Start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris, France) (SIGIR’19). Association for Computing Machinery, New York, NY, USA, 695–704. https://doi.org/10.1145/3331184.3331268
  25. Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data. ACM Trans. Inf. Syst. 37, 1, Article 5 (oct 2018), 35 pages. https://doi.org/10.1145/3233770
  26. Steffen Rendle. 2010. Factorization Machines. 2010 IEEE International Conference on Data Mining (2010), 995–1000. https://api.semanticscholar.org/CorpusID:17265929
  27. Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Long Beach, CA, USA) (KDD ’23). Association for Computing Machinery, New York, NY, USA, 4813–4822. https://doi.org/10.1145/3580305.3599851
  28. One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (Virtual Event, Queensland, Australia) (CIKM ’21). Association for Computing Machinery, New York, NY, USA, 4104–4113. https://doi.org/10.1145/3459637.3481941
  29. Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations. In Proceedings of the 14th ACM Conference on Recommender Systems (Virtual Event, Brazil) (RecSys ’20). Association for Computing Machinery, New York, NY, USA, 269–278. https://doi.org/10.1145/3383313.3412236
  30. Attention is All You Need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS’17). Curran Associates Inc., Red Hook, NY, USA, 6000–6010.
  31. DropoutNet: Addressing Cold Start in Recommender Systems. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS’17). Curran Associates Inc., Red Hook, NY, USA, 4964–4973.
  32. RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (Torino, Italy) (CIKM ’18). Association for Computing Machinery, New York, NY, USA, 417–426. https://doi.org/10.1145/3269206.3271739
  33. Adversarial gradient driven exploration for deep click-through rate prediction. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2050–2058.
  34. Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. In The World Wide Web Conference (San Francisco, CA, USA) (WWW ’19). Association for Computing Machinery, New York, NY, USA, 2091–2102. https://doi.org/10.1145/3308558.3313442
  35. Modelling of Bi-Directional Spatio-Temporal Dependence and Users’ Dynamic Preferences for Missing POI Check-in Identification. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence (Honolulu, Hawaii, USA) (AAAI’19/IAAI’19/EAAI’19). AAAI Press, Article 669, 8 pages. https://doi.org/10.1609/aaai.v33i01.33015458
  36. Internal and Contextual Attention Network for Cold-Start Multi-Channel Matching in Recommendation. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (Yokohama, Yokohama, Japan) (IJCAI’20). Article 379, 7 pages.
  37. Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling. In AAAI Conference on Artificial Intelligence. https://api.semanticscholar.org/CorpusID:227333826
  38. KEEP: An industrial pre-training framework for online recommendation via knowledge extraction and plugging. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 3684–3693.
  39. Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Models. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (Atlanta, GA, USA) (CIKM ’22). Association for Computing Machinery, New York, NY, USA, 2671–2680. https://doi.org/10.1145/3511808.3557479
  40. Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4981–4987.
  41. Deep Interest Evolution Network for Click-through Rate Prediction. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence (Honolulu, Hawaii, USA) (AAAI’19/IAAI’19/EAAI’19). AAAI Press, Article 729, 8 pages. https://doi.org/10.1609/aaai.v33i01.33015941
  42. Deep Interest Network for Click-Through Rate Prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (London, United Kingdom) (KDD ’18). Association for Computing Machinery, New York, NY, USA, 1059–1068. https://doi.org/10.1145/3219819.3219823
  43. S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization. ACM. https://doi.org/10.1145/3340531.3411954
  44. Learning to Warm Up Cold Item Embeddings for Cold-Start Recommendation with Meta Scaling and Shifting Networks. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, Canada) (SIGIR ’21). Association for Computing Machinery, New York, NY, USA, 1167–1176. https://doi.org/10.1145/3404835.3462843
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

Whiteboard

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

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

Continue Learning

We haven't generated follow-up questions for this paper yet.

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

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up

Tweets

Sign up for free to view the 1 tweet with 8 likes about this paper.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up for Free