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Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other?

Published: 08 October 2024 Publication History

Abstract

Generative retrieval for search and recommendation is a promising paradigm for retrieving items, offering an alternative to traditional methods that depend on external indexes and nearest-neighbor searches. Instead, generative models directly associate inputs with item IDs. Given the breakthroughs of Large Language Models (LLMs), these generative systems can play a crucial role in centralizing a variety of Information Retrieval (IR) tasks in a single model that performs tasks such as query understanding, retrieval, recommendation, explanation, re-ranking, and response generation. Despite the growing interest in such a unified generative approach for IR systems, the advantages of using a single, multi-task model over multiple specialized models are not well established in the literature. This paper investigates whether and when such a unified approach can outperform task-specific models in the IR tasks of search and recommendation, broadly co-existing in multiple industrial online platforms, such as Spotify, YouTube, and Netflix. Previous work shows that (1) the latent representations of items learned by generative recommenders are biased towards popularity, and (2) content-based and collaborative-filtering-based information can improve an item’s representations. Motivated by this, our study is guided by two hypotheses: [H1] the joint training regularizes the estimation of each item’s popularity, and [H2] the joint training regularizes the item’s latent representations, where search captures content-based aspects of an item and recommendation captures collaborative-filtering aspects. Our extensive experiments with both simulated and real-world data support both [H1] and [H2] as key contributors to the effectiveness improvements observed in the unified search and recommendation generative models over the single-task approaches.

References

[1]
Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, Dzmitry Bahdanau, Nicolas Chapados, and Siva Reddy. 2024. LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders. arXiv preprint arXiv:2404.05961 (2024).
[2]
Nicholas J Belkin and W Bruce Croft. 1992. Information filtering and information retrieval: Two sides of the same coin?Commun. ACM 35, 12 (1992), 29–38.
[3]
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Jimmy Lin, Ellen M. Voorhees, and Ian Soboroff. 2023. Overview of the TREC 2022 deep learning track. In Text REtrieval Conference (TREC). NIST, TREC. https://www.microsoft.com/en-us/research/publication/overview-of-the-trec-2022-deep-learning-track/
[4]
Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni. 2020. Autoregressive entity retrieval. arXiv preprint arXiv:2010.00904 (2020).
[5]
Zhicheng Dou, Ruihua Song, and Ji-Rong Wen. 2007. A large-scale evaluation and analysis of personalized search strategies. In Proceedings of the 16th international conference on World Wide Web. 581–590.
[6]
Guglielmo Faggioli, Laura Dietz, Charles LA Clarke, Gianluca Demartini, Matthias Hagen, Claudia Hauff, Noriko Kando, Evangelos Kanoulas, Martin Potthast, Benno Stein, 2023. Perspectives on large language models for relevance judgment. In Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval. 39–50.
[7]
Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Jiliang Tang, and Qing Li. 2023. Recommender systems in the era of large language models (llms). arXiv preprint arXiv:2307.02046 (2023).
[8]
Thibault Formal, Carlos Lassance, Benjamin Piwowarski, and Stéphane Clinchant. 2021. SPLADE v2: Sparse lexical and expansion model for information retrieval. arXiv preprint arXiv:2109.10086 (2021).
[9]
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022. 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.
[10]
K George. [n. d.]. Zipf. 1935. The psycho-biology of language.
[11]
Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli. 2023. ChatGPT outperforms crowd workers for text-annotation tasks. Proceedings of the National Academy of Sciences 120, 30 (2023), e2305016120.
[12]
Yuqi Gong, Xichen Ding, Yehui Su, Kaiming Shen, Zhongyi Liu, and Guannan Zhang. 2023. An Unified Search and Recommendation Foundation Model for Cold-Start Scenario. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4595–4601.
[13]
F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5, 4 (2015), 1–19.
[14]
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).
[15]
Wenyue Hua, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. 2023. How to Index Item IDs for Recommendation Foundation Models. arXiv preprint arXiv:2305.06569 (2023).
[16]
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2021. Unsupervised dense information retrieval with contrastive learning. arXiv preprint arXiv:2112.09118 (2021).
[17]
Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM). IEEE, 197–206.
[18]
Wang-Cheng Kang and Julian McAuley. 2018. Self-Attentive Sequential Recommendation. arxiv:1808.09781 [cs.IR]
[19]
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense passage retrieval for open-domain question answering. arXiv preprint arXiv:2004.04906 (2020).
[20]
Varsha Kishore, Chao Wan, Justin Lovelace, Yoav Artzi, and Kilian Q Weinberger. 2023. IncDSI: Incrementally Updatable Document Retrieval. (2023).
[21]
Solomon Kullback and Richard A Leibler. 1951. On information and sufficiency. The annals of mathematical statistics 22, 1 (1951), 79–86.
[22]
Carlos Lassance, Hervé Déjean, Thibault Formal, and Stéphane Clinchant. 2024. SPLADE-v3: New baselines for SPLADE. arXiv preprint arXiv:2403.06789 (2024).
[23]
Yongqi Li, Xinyu Lin, Wenjie Wang, Fuli Feng, Liang Pang, Wenjie Li, Liqiang Nie, Xiangnan He, and Tat-Seng Chua. 2024. A Survey of Generative Search and Recommendation in the Era of Large Language Models. arxiv:2404.16924 [cs.IR]
[24]
Yongqi Li, Nan Yang, Liang Wang, Furu Wei, and Wenjie Li. 2023. Multiview Identifiers Enhanced Generative Retrieval. arXiv preprint arXiv:2305.16675 (2023).
[25]
Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, 2023. How can recommender systems benefit from large language models: A survey. arXiv preprint arXiv:2306.05817 (2023).
[26]
Jimmy Lin, Rodrigo Nogueira, and Andrew Yates. 2022. Pretrained transformers for text ranking: Bert and beyond. Springer Nature.
[27]
Qijiong Liu, Nuo Chen, Tetsuya Sakai, and Xiao-Ming Wu. 2024. Once: Boosting content-based recommendation with both open-and closed-source large language models. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining. 452–461.
[28]
Zhenghao Liu, Sen Mei, Chenyan Xiong, Xiaohua Li, Shi Yu, Zhiyuan Liu, Yu Gu, and Ge Yu. 2023. Text Matching Improves Sequential Recommendation by Reducing Popularity Biases. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 1534–1544.
[29]
Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017).
[30]
Craig Macdonald, Nicola Tonellotto, Sean MacAvaney, and Iadh Ounis. 2021. PyTerrier: Declarative experimentation in Python from BM25 to dense retrieval., 4526–4533 pages.
[31]
Sanket Vaibhav Mehta, Jai Gupta, Yi Tay, Mostafa Dehghani, Vinh Q Tran, Jinfeng Rao, Marc Najork, Emma Strubell, and Donald Metzler. 2022. DSI++: Updating Transformer Memory with New Documents. arXiv preprint arXiv:2212.09744 (2022).
[32]
Donald Metzler, Yi Tay, Dara Bahri, and Marc Najork. 2021. Rethinking search: making domain experts out of dilettantes. In Acm sigir forum, Vol. 55. ACM New York, NY, USA, 1–27.
[33]
Thong Nguyen, Sean MacAvaney, and Andrew Yates. 2023. A Unified Framework for Learned Sparse Retrieval. In European Conference on Information Retrieval. Springer, 101–116.
[34]
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. Ms marco: A human-generated machine reading comprehension dataset. (2016).
[35]
Rodrigo Nogueira and Kyunghyun Cho. 2019. Passage Re-ranking with BERT. arXiv preprint arXiv:1901.04085 (2019).
[36]
Govindarajan Parthasarathy and Shanmugam Sathiya Devi. 2023. Hybrid Recommendation System Based on Collaborative and Content-Based Filtering. Cybernetics and Systems 54, 4 (2023), 432–453.
[37]
Gustavo Penha and Claudia Hauff. 2020. What does bert know about books, movies and music? probing bert for conversational recommendation. In Proceedings of the 14th ACM conference on recommender systems. 388–397.
[38]
Gustavo Penha, Enrico Palumbo, Maryam Aziz, Alice Wang, and Hugues Bouchard. 2023. Improving content retrievability in search with controllable query generation. In Proceedings of the ACM Web Conference 2023. 3182–3192.
[39]
Ronak Pradeep, Kai Hui, Jai Gupta, Adam D Lelkes, Honglei Zhuang, Jimmy Lin, Donald Metzler, and Vinh Q Tran. 2023. How Does Generative Retrieval Scale to Millions of Passages?arXiv preprint arXiv:2305.11841 (2023).
[40]
Ronak Pradeep, Sahel Sharifymoghaddam, and Jimmy Lin. 2023. Rankvicuna: Zero-shot listwise document reranking with open-source large language models. arXiv preprint arXiv:2309.15088 (2023).
[41]
Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, 2023. Large language models are effective text rankers with pairwise ranking prompting. arXiv preprint arXiv:2306.17563 (2023).
[42]
Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence. 2022. Dataset shift in machine learning. Mit Press.
[43]
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research 21, 140 (2020), 1–67.
[44]
Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan H Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q Tran, Jonah Samost, 2023. Recommender Systems with Generative Retrieval. arXiv preprint arXiv:2305.05065 (2023).
[45]
Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. https://arxiv.org/abs/1908.10084
[46]
Stephen E Robertson and Steve Walker. 1994. Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In SIGIR’94. Springer, 232–241.
[47]
Teng Shi, Zihua Si, Jun Xu, Xiao Zhang, Xiaoxue Zang, Kai Zheng, Dewei Leng, Yanan Niu, and Yang Song. 2024. UniSAR: Modeling User Transition Behaviors between Search and Recommendation. arXiv preprint arXiv:2404.09520 (2024).
[48]
Zihua Si, Zhongxiang Sun, Xiao Zhang, Jun Xu, Xiaoxue Zang, Yang Song, Kun Gai, and Ji-Rong Wen. 2023. When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation. arXiv preprint arXiv:2305.10822 (2023).
[49]
Ítallo Silva, Leandro Marinho, Alan Said, and Martijn C Willemsen. 2024. Leveraging ChatGPT for Automated Human-centered Explanations in Recommender Systems. In Proceedings of the 29th International Conference on Intelligent User Interfaces. 597–608.
[50]
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2020. Mpnet: Masked and permuted pre-training for language understanding. Advances in Neural Information Processing Systems 33 (2020), 16857–16867.
[51]
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management. 1441–1450.
[52]
Yi Tay, Vinh Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Gupta, 2022. Transformer memory as a differentiable search index. Advances in Neural Information Processing Systems 35 (2022), 21831–21843.
[53]
Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych. 2021. Beir: A heterogenous benchmark for zero-shot evaluation of information retrieval models. arXiv preprint arXiv:2104.08663 (2021).
[54]
Poonam B Thorat, Rajeshwari M Goudar, and Sunita Barve. 2015. Survey on collaborative filtering, content-based filtering and hybrid recommendation system. International Journal of Computer Applications 110, 4 (2015), 31–36.
[55]
Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE.Journal of machine learning research 9, 11 (2008).
[56]
Jesse Vig, Shilad Sen, and John Riedl. 2012. The tag genome: Encoding community knowledge to support novel interaction. ACM Transactions on Interactive Intelligent Systems (TiiS) 2, 3 (2012), 1–44.
[57]
Ashwin K Vijayakumar, Michael Cogswell, Ramprasath R Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. 2016. Diverse beam search: Decoding diverse solutions from neural sequence models. arXiv preprint arXiv:1610.02424 (2016).
[58]
Jian Wang, Yi Zhang, and Tao Chen. 2012. Unified recommendation and search in e-commerce. In Information Retrieval Technology: 8th Asia Information Retrieval Societies Conference, AIRS 2012, Tianjin, China, December 17-19, 2012. Proceedings 8. Springer, 296–305.
[59]
Wenjie Wang, Yiyan Xu, Fuli Feng, Xinyu Lin, Xiangnan He, and Tat-Seng Chua. 2023. Diffusion recommender model. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 832–841.
[60]
Xiaolei Wang, Xinyu Tang, Wayne Xin Zhao, Jingyuan Wang, and Ji-Rong Wen. 2023. Rethinking the evaluation for conversational recommendation in the era of large language models. arXiv preprint arXiv:2305.13112 (2023).
[61]
Yujing Wang, Yingyan Hou, Haonan Wang, Ziming Miao, Shibin Wu, Qi Chen, Yuqing Xia, Chengmin Chi, Guoshuai Zhao, Zheng Liu, 2022. A neural corpus indexer for document retrieval. Advances in Neural Information Processing Systems 35 (2022), 25600–25614.
[62]
Zihan Wang, Yujia Zhou, Yiteng Tu, and Zhicheng Dou. 2023. NOVO: Learnable and Interpretable Document Identifiers for Model-Based IR. (2023).
[63]
Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. 2021. Empowering news recommendation with pre-trained language models. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 1652–1656.
[64]
Jun Xu, Xiangnan He, and Hang Li. 2018. Deep learning for matching in search and recommendation. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 1365–1368.
[65]
Tianchi Yang, Minghui Song, Zihan Zhang, Haizhen Huang, Weiwei Deng, Feng Sun, and Qi Zhang. 2023. Auto Search Indexer for End-to-End Document Retrieval. arXiv preprint arXiv:2310.12455 (2023).
[66]
Jing Yao, Zhicheng Dou, Ruobing Xie, Yanxiong Lu, Zhiping Wang, and Ji-Rong Wen. 2021. USER: A unified information search and recommendation model based on integrated behavior sequence. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2373–2382.
[67]
Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, and Ed Chi. 2019. Sampling-bias-corrected neural modeling for large corpus item recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems. 269–277.
[68]
Kai Yu, Anton Schwaighofer, Volker Tresp, Wei-Ying Ma, and HongJiang Zhang. 2012. Collaborative ensemble learning: Combining collaborative and content-based information filtering via hierarchical Bayes. arXiv preprint arXiv:1212.2508 (2012).
[69]
Hamed Zamani and W Bruce Croft. 2018. Joint modeling and optimization of search and recommendation. arXiv preprint arXiv:1807.05631 (2018).
[70]
Hamed Zamani and W Bruce Croft. 2020. Learning a joint search and recommendation model from user-item interactions. In Proceedings of the 13th international conference on web search and data mining. 717–725.
[71]
Hamed Zamani, Markus Schedl, Paul Lamere, and Ching-Wei Chen. 2019. An analysis of approaches taken in the acm recsys challenge 2018 for automatic music playlist continuation. ACM Transactions on Intelligent Systems and Technology (TIST) 10, 5 (2019), 1–21.
[72]
Hansi Zeng, Chen Luo, Bowen Jin, Sheikh Muhammad Sarwar, Tianxin Wei, and Hamed Zamani. 2023. Scalable and Effective Generative Information Retrieval. arXiv preprint arXiv:2311.09134 (2023).
[73]
Hansi Zeng, Chen Luo, and Hamed Zamani. 2024. Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decoding. arxiv:2404.14600 [cs.IR]
[74]
Hailin Zhang, Yujing Wang, Qi Chen, Ruiheng Chang, Ting Zhang, Ziming Miao, Yingyan Hou, Yang Ding, Xupeng Miao, Haonan Wang, 2023. Model-enhanced Vector Index. arXiv preprint arXiv:2309.13335 (2023).
[75]
Kai Zhao, Yukun Zheng, Tao Zhuang, Xiang Li, and Xiaoyi Zeng. 2022. Joint learning of e-commerce search and recommendation with a unified graph neural network. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 1461–1469.
[76]
Wayne Xin Zhao, Yupeng Hou, Xingyu Pan, Chen Yang, Zeyu Zhang, Zihan Lin, Jingsen Zhang, Shuqing Bian, Jiakai Tang, Wenqi Sun, Yushuo Chen, Lanling Xu, Gaowei Zhang, Zhen Tian, Changxin Tian, Shanlei Mu, Xinyan Fan, Xu Chen, and Ji-Rong Wen. 2022. RecBole 2.0: Towards a More Up-to-Date Recommendation Library. In CIKM. ACM, 4722–4726.

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              RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
              October 2024
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              Published: 08 October 2024

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              1. Generative Recommendation
              2. Generative Retrieval
              3. Joint Search and Recommendation
              4. Multi-task Learning

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