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Embedding-based retrieval serves as a dominant approach to candidate item matching for industrial recommender systems.
Autoregressive Entity Retrieval
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UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation. In The 30th ACM International Conference on Information and Knowledge Management (CIKM) . 1253–1262
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Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models
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Cross-Batch Negative Sampling for Training Two-Tower Recommenders. In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) . 1632–1636
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Recommender Forest for Efficient Retrieval
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Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In Proceedings of the 16th ACM Conference on Recommender Systems . 299–315
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Autoregressive image generation using residual quantization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 11523–11532
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Transformer Memory as a Differentiable Search Index
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Text Embeddings by Weakly-Supervised Contrastive Pre-training
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Matthijs Douze, Alexandr Guzhva, Chengqi Deng, Jeff Johnson, Gergely Szilvasy, Pierre-Emmanuel Mazaré, Maria Lomeli, Lucas Hosseini, and Hervé Jégou. 2024 · 2024
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On the Role of Discrete Tokenization in Visual Representation Learning. In The Twelfth International Conference on Learning Representations (ICLR)
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Bridging Items and Language: A Transition Paradigm for Large Language Model-Based Recommendation. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) . 1816–1826
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A Large Language Model Enhanced Conversational Recommender System
Yue Feng, Shuchang Liu, Zhenghai Xue, Qingpeng Cai, Lantao Hu, Peng Jiang, Kun Gai, and Fei Sun. 2023 · 2023
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How to Index Item IDs for Recommendation Foundation Models
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Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks. In International Conference on Machine Learning (ICML) . 14096–14113
Minyoung Huh, Brian Cheung, Pulkit Agrawal, and Phillip Isola. 2023 · 2023
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FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation. In Proceedings of the ACM Web Conference (WWW) . 3309–3318
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LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee’s Advertisement Recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems (RecSys) . 334–337
Dang Minh Nguyen, Chenfei Wang, Yan Shen, and Yifan Zeng. 2023 · 2023
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Recommender Systems with Generative Retrieval. In Annual Conference on Neural Information Processing Systems (NeurIPS)
Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q. Tran, Jonah Samost, Maciej Kula, Ed H. Chi, and Mahesh Sathiamoorthy. 2023 · 2023
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Better Generalization with Semantic IDs: A case study in Ranking for Recommendations
Anima Singh, Trung Vu, Raghunandan Keshavan, Nikhil Mehta, Xinyang Yi, Lichan Hong, Lukasz Heldt, Li Wei, Ed Chi, and Maheswaran Sathiamoorthy. 2023 · 2023
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Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin, Jieming Zhu, Bo Chen, Ruiming Tang, Weinan Zhang, Rui Zhang, and Yong Yu. 2023 · 2023
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Qijiong Liu, Xiaoyu Dong, Jiaren Xiao, Nuo Chen, Hengchang Hu, Jieming Zhu, Chenxu Zhu, Tetsuya Sakai, and Xiao-Ming Wu. 2024a · 2024
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LLM-generated Explanations for Recommender Systems. In Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP)
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TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendation
Haohao Qu, Wenqi Fan, Zihuai Zhao, and Qing Li. 2024 · 2024
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Non-autoregressive Generative Models for Reranking Recommendation
Yuxin Ren, Qiya Yang, Yichun Wu, Wei Xu, Yalong Wang, and Zhiqiang Zhang. 2024 · 2024
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Recent Advances in Generative Information Retrieval. In Companion Proceedings of the ACM on Web Conference (WWW) . 1238–1241
Yubao Tang, Ruqing Zhang, Weiwei Sun, Jiafeng Guo, and Maarten de Rijke. 2024 · 2024
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Learnable Item Tokenization for Generative Recommendation
Wenjie Wang, Honghui Bao, Xilin Chen, Jizhi Zhang, Yongqi Li, Fuli Feng, See-Kiong Ng, and Tat-Seng Chua. 2024a · 2024
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NoteLLM: A Retrievable Large Language Model for Note Recommendation. In Companion Proceedings of the ACM on Web Conference (WWW) . 170–179
Chao Zhang, Shiwei Wu, Haoxin Zhang, Tong Xu, Yan Gao, Yao Hu, and Enhong Chen. 2024 · 2024
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Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation. In 40th IEEE International Conference on Data Engineering (ICDE) . 1435–1448
Bowen Zheng, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, Ming Chen, and Ji-Rong Wen. 2024 · 2024
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Improved Vector Quantization For Dense Retrieval with Contrastive Distillation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) . 2072–2076
James O’Neill and Sourav Dutta. 2023 · 2076
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