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Pfeed: Generating near real-time personalized feeds using precomputed embedding similarities (2402.16073v2)

Published 25 Feb 2024 in cs.IR, cs.AI, and cs.LG

Abstract: In personalized recommender systems, embeddings are often used to encode customer actions and items, and retrieval is then performed in the embedding space using approximate nearest neighbor search. However, this approach can lead to two challenges: 1) user embeddings can restrict the diversity of interests captured and 2) the need to keep them up-to-date requires an expensive, real-time infrastructure. In this paper, we propose a method that overcomes these challenges in a practical, industrial setting. The method dynamically updates customer profiles and composes a feed every two minutes, employing precomputed embeddings and their respective similarities. We tested and deployed this method to personalise promotional items at Bol, one of the largest e-commerce platforms of the Netherlands and Belgium. The method enhanced customer engagement and experience, leading to a significant 4.9% uplift in conversions.

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References (32)
  1. ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest. 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, 2703–2711. https://doi.org/10.1145/3534678.3539170
  2. Controllable Multi-Interest Framework for Recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Virtual Event, CA, USA) (KDD ’20). Association for Computing Machinery, New York, NY, USA, 2942–2951. https://doi.org/10.1145/3394486.3403344
  3. 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
  4. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems. 191–198.
  5. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186. https://doi.org/10.18653/v1/N19-1423
  6. 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.
  7. Accelerating Large-Scale Inference with Anisotropic Vector Quantization. https://arxiv.org/abs/1908.10396
  8. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 173–182.
  9. Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM’08. Eighth IEEE International Conference on. Ieee, 263–272.
  10. TencentRec: Real-Time Stream Recommendation in Practice. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (Melbourne, Victoria, Australia) (SIGMOD ’15). Association for Computing Machinery, New York, NY, USA, 227–238. https://doi.org/10.1145/2723372.2742785
  11. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data 7, 3 (2019), 535–547.
  12. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009).
  13. Advances in collaborative filtering. Recommender systems handbook (2022), 91–142.
  14. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems, F. Pereira, C.J. Burges, L. Bottou, and K.Q. Weinberger (Eds.), Vol. 25. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
  15. Taku Kudo and John Richardson. 2018. SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, Brussels, Belgium, 66–71. https://doi.org/10.18653/v1/D18-2012
  16. Multi-Interest Network with Dynamic Routing for Recommendation at Tmall. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (Beijing, China) (CIKM ’19). Association for Computing Machinery, New York, NY, USA, 2615–2623. https://doi.org/10.1145/3357384.3357814
  17. Text Is All You Need: Learning Language Representations for Sequential Recommendation. 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, 1258–1267. https://doi.org/10.1145/3580305.3599519
  18. PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Virtual Event, CA, USA) (KDD ’20). Association for Computing Machinery, New York, NY, USA, 2311–2320. https://doi.org/10.1145/3394486.3403280
  19. PinnerFormer: Sequence Modeling for User Representation at Pinterest. 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, 3702–3712. https://doi.org/10.1145/3534678.3539156
  20. Xiaoyuan Su and Taghi M Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence 2009 (2009).
  21. Attention is All you Need. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
  22. Billion-Scale Commodity Embedding for E-Commerce Recommendation in Alibaba. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (London, United Kingdom) (KDD ’18). Association for Computing Machinery, New York, NY, USA, 839–848. https://doi.org/10.1145/3219819.3219869
  23. DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-Scale Learning to Rank Systems. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW ’21). Association for Computing Machinery, New York, NY, USA, 1785–1797. https://doi.org/10.1145/3442381.3450078
  24. Personalized embedding-based e-commerce recommendations at ebay. arXiv preprint arXiv:2102.06156 (2021).
  25. Mixed Negative Sampling for Learning Two-Tower Neural Networks in Recommendations. In Companion Proceedings of the Web Conference 2020 (Taipei, Taiwan) (WWW ’20). Association for Computing Machinery, New York, NY, USA, 441–447. https://doi.org/10.1145/3366424.3386195
  26. Mixed negative sampling for learning two-tower neural networks in recommendations. In Companion Proceedings of the Web Conference 2020. 441–447.
  27. Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations (RecSys ’19). Association for Computing Machinery, New York, NY, USA, 269–277. https://doi.org/10.1145/3298689.3346996
  28. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (London, United Kingdom) (KDD ’18). Association for Computing Machinery, New York, NY, USA, 974–983. https://doi.org/10.1145/3219819.3219890
  29. Large batch optimization for deep learning: Training bert in 76 minutes. arXiv preprint arXiv:1904.00962 (2019).
  30. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR) 52, 1 (2019), 1–38.
  31. Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta. arXiv preprint arXiv:2311.09544 (2023).
  32. Deep Interest Network for Click-Through Rate Prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (London, United Kingdom) (KDD ’18). Association for Computing Machinery, New York, NY, USA, 1059–1068. https://doi.org/10.1145/3219819.3219823

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