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With the information explosion on the Web, search and recommendation are foundational infrastructures to satisfying users' information needs.
Information filtering and information retrieval: Two sides of the same coin?
Nicholas J Belkin and W Bruce Croft. 1992 · 1992
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Empirical analysis of predictive algorithms for collaborative filtering. In UAI . 43–52
John S Breese, David Heckerman, and Carl Kadie. 1998 · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998 · 1998
Earlier work this paper cites.
An algorithmic framework for performing collaborative filtering. In SIGIR . ACM, 230–237
Jonathan L Herlocker, Joseph A Konstan, Al Borchers, and John Riedl. 1999 · 1999
Earlier work this paper cites.
Opportunistic data structures with applications. In Proceedings 41st annual symposium on foundations of computer science . IEEE, 390–398
Paolo Ferragina and Giovanni Manzini. 2000 · 2000
Earlier work this paper cites.
Information retrieval on the web
Mei Kobayashi and Koichi Takeda. 2000 · 2000
Earlier work this paper cites.
Algorithms for non-negative matrix factorization
Daniel Lee and H Sebastian Seung. 2000 · 2000
Earlier work this paper cites.
Item-based collaborative filtering recommendation algorithms. In WWW . ACM, 285–295
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001 · 2001
Earlier work this paper cites.
Ranking with large margin principle: Two approaches
Amnon Shashua and Anat Levin. 2002 · 2002
Earlier work this paper cites.
An efficient boosting algorithm for combining preferences
Yoav Freund, Raj Iyer, Robert E Schapire, and Yoram Singer. 2003 · 2003
Earlier work this paper cites.
Amazon. com recommendations: Item-to-item collaborative filtering
Greg Linden, Brent Smith, and Jeremy York. 2003 · 2003
Earlier work this paper cites.
Discriminative models for information retrieval. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval . 64–71
Ramesh Nallapati. 2004 · 2004
Earlier work this paper cites.
Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions
Gediminas Adomavicius and Alexander Tuzhilin. 2005 · 2005
Earlier work this paper cites.
Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning . 89–96
Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. 2005 · 2005
Earlier work this paper cites.
New approaches to support vector ordinal regression. In Proceedings of the 22nd international conference on Machine learning . 145–152
Wei Chu and S Sathiya Keerthi. 2005 · 2005
Earlier work this paper cites.
Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th international conference on Machine learning . 129–136
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007 · 2007
Earlier work this paper cites.
Ranking with multiple hyperplanes. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval . 279–286
Tao Qin, Xu-Dong Zhang, De-Sheng Wang, Tie-Yan Liu, Wei Lai, and Hang Li. 2007 · 2007
Earlier work this paper cites.
Frank: a ranking method with fidelity loss. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval . 383–390
Ming-Feng Tsai, Tie-Yan Liu, Tao Qin, Hsin-Hsi Chen, and Wei-Ying Ma. 2007 · 2007
Earlier work this paper cites.
Query-level loss functions for information retrieval
Tao Qin, Xu-Dong Zhang, Ming-Feng Tsai, De-Sheng Wang, Tie-Yan Liu, and Hang Li. 2008 · 2008
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008 · 2008
Earlier work this paper cites.
Listwise approach to learning to rank: theory and algorithm. In Proceedings of the 25th international conference on Machine learning . 1192–1199
Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. 2008 · 2008
Earlier work this paper cites.
Supervised semantic indexing. In Proceedings of the 18th ACM conference on Information and knowledge management . 187–196
Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Kunihiko Sadamasa, Yanjun Qi, Olivier Chapelle, and Kilian Weinberger. 2009 · 2009
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
Learning to rank for information retrieval
Tie-Yan Liu et al · 2009
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback. In UAI . AUAI Press, 452–461
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Earlier work this paper cites.
Learning to rank with (a lot of) word features
Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Kunihiko Sadamasa, Yanjun Qi, Olivier Chapelle, and Kilian Weinberger. 2010 · 2010
Earlier work this paper cites.
Factorization Machines. In ICDM . IEEE, 995–1000
Steffen Rendle. 2010 · 2010
Earlier work this paper cites.
Information seeking: convergence of search, recommendations, and advertising
Hector Garcia-Molina, Georgia Koutrika, and Aditya Parameswaran. 2011 · 2011
Earlier work this paper cites.
Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management . 2333–2338
Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013 · 2013
Earlier work this paper cites.
Fism: factored item similarity models for top-n recommender systems. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining . 659–667
Santosh Kabbur, Xia Ning, and George Karypis. 2013 · 2013
Earlier work this paper cites.
Reasoning with neural tensor networks for knowledge base completion
Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng. 2013 · 2013
Earlier work this paper cites.
Learning Bilinear Model for Matching Queries and Documents
Wei Wu, Zhengdong Lu, and Hang Li. 2013 · 2013
Earlier work this paper cites.
Convolutional neural network architectures for matching natural language sentences
Baotian Hu, Zhengdong Lu, Hang Li, and Qingcai Chen. 2014 · 2014
Earlier work this paper cites.
A latent semantic model with convolutional-pooling structure for information retrieval. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management . 101–110
Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil. 2014 · 2014
Earlier work this paper cites.
Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval
Hamid Palangi, Li Deng, Yelong Shen, Jianfeng Gao, Xiaodong He, Jianshu Chen, Xinying Song, and Rabab Ward. 2016 · 2016
Earlier work this paper cites.
Neural Collaborative Filtering. In WWW . ACM, 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Earlier work this paper cites.
Multi-granularity neural sentence model for measuring short text similarity. In Database Systems for Advanced Applications: 22nd International Conference, DASFAA 2017, Suzhou, China, March 27-30, 2017, Proceedings, Part I 22 . Springer, 439–455
Jiangping Huang, Shuxin Yao, Chen Lyu, and Donghong Ji. 2017 · 2017
Earlier work this paper cites.
Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Deep matrix factorization models for recommender systems.. In IJCAI , Vol. 17. Melbourne, Australia, 3203–3209
Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017 · 2017
Earlier work this paper cites.
Joint representation learning for top-n recommendation with heterogeneous information sources. In CIKM . 1449–1458
Yongfeng Zhang, Qingyao Ai, Xu Chen, and W Bruce Croft. 2017 · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Self-attentive sequential recommendation. In ICDM . IEEE, 197–206
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Earlier work this paper cites.
Personalized top-n sequential recommendation via convolutional sequence embedding. In WSDM . ACM, 565–573
Jiaxi Tang and Ke Wang. 2018 · 2018
Earlier work this paper cites.
User profile feature-based approach to address the cold start problem in collaborative filtering for personalized movie recommendation. In ICDIM . IEEE, 24–28
Lasitha Uyangoda, Supunmali Ahangama, and Tharindu Ranasinghe. 2018 · 2018
Earlier work this paper cites.
The lambdaloss framework for ranking metric optimization. In Proceedings of the 27th ACM international conference on information and knowledge management . 1313–1322
Xuanhui Wang, Cheng Li, Nadav Golbandi, Michael Bendersky, and Marc Najork. 2018 · 2018
Earlier work this paper cites.
Dynamic explainable recommendation based on neural attentive models. In AAAI , Vol. 33. 53–60
Xu Chen, Yongfeng Zhang, and Zheng Qin. 2019 · 2019
Cited alongside, same era.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 2019
Cited alongside, same era.
BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In CIKM . ACM, 1441–1450
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 2019
Cited alongside, same era.
Neural Graph Collaborative Filtering. In SIGIR . ACM, 165–174
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019 · 2019
Cited alongside, same era.
Autoregressive Entity Retrieval. In International Conference on Learning Representations
Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni. 2020 · 2020
Generative sequential recommendation with gptrec
Aleksandr V Petrov and Craig Macdonald. 2023 · 2023
Later among the works it cites.
How Does Generative Retrieval Scale to Millions of Passages?
Ronak Pradeep, Kai Hui, Jai Gupta, Adam D Lelkes, Honglei Zhuang, Jimmy Lin, Donald Metzler, and Vinh Q Tran. 2023 · 2023
Later among the works it cites.
Large language models are effective text rankers with pairwise ranking prompting
Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, et al · 2023
Later among the works it cites.
ControlRec: Bridging the semantic gap between language model and personalized recommendation
Junyan Qiu, Haitao Wang, Zhaolin Hong, Yiping Yang, Qiang Liu, and Xingxing Wang. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
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Cited alongside, same era.
Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020 · 2020
Cited alongside, same era.
Lightgcn: Simplifying and powering graph convolution network for recommendation. In SIGIR . 639–648
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
Cited alongside, same era.
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
Cited alongside, same era.
Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Cited alongside, same era.
Deep Learning for Matching in Search and Recommendation
Jun Xu, Xiangnan He, and Hang Li. 2020 · 2020
Cited alongside, same era.
Soundstream: An end-to-end neural audio codec
Neil Zeghidour, Alejandro Luebs, Ahmed Omran, Jan Skoglund, and Marco Tagliasacchi. 2021 · 2021
Cited alongside, same era.
Recommender Systems with Generative Retrieval. In NeurIPS . Curran Associates, Inc
Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan H Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q Tran, Jonah Samost, et al · 2023
Later among the works it cites.
TOME: A Two-stage Approach for Model-based Retrieval
Ruiyang Ren, Wayne Xin Zhao, Jing Liu, Hua Wu, Ji-Rong Wen, and Haifeng Wang. 2023b · 2023
Later among the works it cites.
Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning
Zihua Si, Zhongxiang Sun, Jiale Chen, Guozhang Chen, Xiaoxue Zang, Kai Zheng, Yang Song, Xiao Zhang, and Jun Xu. 2023 · 2023
Later among the works it cites.
Learning to Tokenize for Generative Retrieval
Weiwei Sun, Lingyong Yan, Zheng Chen, Shuaiqiang Wang, Haichao Zhu, Pengjie Ren, Zhumin Chen, Dawei Yin, Maarten de Rijke, and Zhaochun Ren. 2023b · 2023
Later among the works it cites.
Is chatgpt good at search? investigating large language models as re-ranking agent
Weiwei Sun, Lingyong Yan, Xinyu Ma, Pengjie Ren, Dawei Yin, and Zhaochun Ren. 2023c · 2023
Later among the works it cites.
Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies
Yubao Tang, Ruqing Zhang, Jiafeng Guo, Jiangui Chen, Zuowei Zhu, Shuaiqiang Wang, Dawei Yin, and Xueqi Cheng. 2023 · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Later among the works it cites.
Zero-Shot Next-Item Recommendation using Large Pretrained Language Models
Lei Wang and Ee-Peng Lim. 2023 · 2023
Later among the works it cites.
Query2doc: Query expansion with large language models
Liang Wang, Nan Yang, and Furu Wei. 2023d · 2023
Later among the works it cites.
A survey on large language models for recommendation
Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, et al · 2023
Later among the works it cites.
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
Later among the works it cites.
Auto Search Indexer for End-to-End Document Retrieval
Tianchi Yang, Minghui Song, Zihan Zhang, Haizhen Huang, Weiwei Deng, Feng Sun, and Qi Zhang. 2023 · 2023
Later among the works it cites.
Heterogeneous knowledge fusion: A novel approach for personalized recommendation via llm. In RecSys . 599–601
Bin Yin, Junjie Xie, Yu Qin, Zixiang Ding, Zhichao Feng, Xiang Li, and Wei Lin. 2023 · 2023
Later among the works it cites.
LlamaRec: Two-stage recommendation using large language models for ranking
Zhenrui Yue, Sara Rabhi, Gabriel de Souza Pereira Moreira, Dong Wang, and Even Oldridge. 2023 · 2023
Later among the works it cites.
Scalable and Effective Generative Information Retrieval
Hansi Zeng, Chen Luo, Bowen Jin, Sheikh Muhammad Sarwar, Tianxin Wei, and Hamed Zamani. 2023 · 2023
Later among the works it cites.
Knowledge prompt-tuning for sequential recommendation. In MM . ACM, 6451–6461
Jianyang Zhai, Xiawu Zheng, Chang-Dong Wang, Hui Li, and Yonghong Tian. 2023 · 2023
Later among the works it cites.
Term-Sets Can Be Strong Document Identifiers For Auto-Regressive Search Engines
Peitian Zhang, Zheng Liu, Yujia Zhou, Zhicheng Dou, and Zhao Cao. 2023c · 2023
Later among the works it cites.
Adapting large language models by integrating collaborative semantics for recommendation
Bowen Zheng, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, and Ji-Rong Wen. 2023 · 2023
Later among the works it cites.
BookGPT: A General Framework for Book Recommendation Empowered by Large Language Model
Aakas Zhiyuli, Yanfang Chen, Xuan Zhang, and Xun Liang. 2023 · 2023
Later among the works it cites.
Enhancing Generative Retrieval with Reinforcement Learning from Relevance Feedback. In The 2023 Conference on Empirical Methods in Natural Language Processing
Yujia Zhou, Zhicheng Dou, and Ji-Rong Wen. 2023 · 2023
Later among the works it cites.
Large Language Models are Built-in Autoregressive Search Engines
Noah Ziems, Wenhao Yu, Zhihan Zhang, and Meng Jiang. 2023 · 2023
Later among the works it cites.
Improving Sequential Recommendations with LLMs
Artun Boz, Wouter Zorgdrager, Zoe Kotti, Jesse Harte, Panos Louridas, Dietmar Jannach, and Marios Fragkoulis. 2024 · 2024
Closest in time.
Aligning Large Language Models with Recommendation Knowledge
Yuwei Cao, Nikhil Mehta, Xinyang Yi, Raghunandan Keshavan, Lukasz Heldt, Lichan Hong, Ed H Chi, and Maheswaran Sathiamoorthy. 2024 · 2024
Closest in time.
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, and Silvia Milano. 2024 · 2024
Closest in time.
Large Language Model with Graph Convolution for Recommendation
Yingpeng Du, Ziyan Wang, Zhu Sun, Haoyan Chua, Hongzhi Liu, Zhonghai Wu, Yining Ma, Jie Zhang, and Youchen Sun. 2024 · 2024
Closest in time.
Integrating Large Language Models with Graphical Session-Based Recommendation
Naicheng Guo, Hongwei Cheng, Qianqiao Liang, Linxun Chen, and Bing Han. 2024 · 2024
Closest in time.
Sein Kim, Hongseok Kang, Seungyoon Choi, Donghyun Kim, Minchul Yang, and Chanyoung Park. 2024 · 2024
Closest in time.
Towards a Unified Language Model for Knowledge-Intensive Tasks Utilizing External Corpus
Xiaoxi Li, Zhicheng Dou, Yujia Zhou, and Fangchao Liu. 2024b · 2024
Closest in time.
Yongqi Li, Wenjie Wang, Leigang Qu, Liqiang Nie, Wenjie Li, and Tat-Seng Chua. 2024d · 2024
Closest in time.
Distillation Enhanced Generative Retrieval
Yongqi Li, Zhen Zhang, Wenjie Wang, Liqiang Nie, Wenjie Li, and Tat-Seng Chua. 2024e · 2024
Closest in time.
Data-efficient Fine-tuning for LLM-based Recommendation. In SIGIR . ACM
Xinyu Lin, Wenjie Wang, Yongqi Li, Shuo Yang, Fuli Feng, Yinwei Wei, and Tat-Seng Chua. 2024 · 2024
Closest in time.
Generative Multi-Modal Knowledge Retrieval with Large Language Models
Xinwei Long, Jiali Zeng, Fandong Meng, Zhiyuan Ma, Kaiyan Zhang, Bowen Zhou, and Jie Zhou. 2024 · 2024
Closest in time.
Aligning Large Language Models for Controllable Recommendations
Wensheng Lu, Jianxun Lian, Wei Zhang, Guanghua Li, Mingyang Zhou, Hao Liao, and Xing Xie. 2024 · 2024
Closest in time.
Generative representational instruction tuning
Niklas Muennighoff, Hongjin Su, Liang Wang, Nan Yang, Furu Wei, Tao Yu, Amanpreet Singh, and Douwe Kiela. 2024 · 2024
Closest in time.
PMG: Personalized Multimodal Generation with Large Language Models. In WWW . ACM
Xiaoteng Shen, Rui Zhang, Xiaoyan Zhao, Jieming Zhu, and Xi Xiao. 2024 · 2024
Closest in time.
Large Language Models Enhanced Collaborative Filtering
Zhongxiang Sun, Zihua Si, Xiaoxue Zang, Kai Zheng, Yang Song, Xiao Zhang, and Jun Xu. 2024 · 2024
Closest in time.
Towards LLM-RecSys Alignment with Textual ID Learning
Juntao Tan, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Zelong Li, and Yongfeng Zhang. 2024 · 2024
Closest in time.
Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
Arpita Vats, Vinija Jain, Rahul Raja, and Aman Chadha. 2024 · 2024
Closest in time.
Towards Universal Multi-Modal Personalization: A Language Model Empowered Generative Paradigm. In ICLR
Tianxin Wei, Bowen Jin, Ruirui Li, Hansi Zeng, Zhengyang Wang, Jianhui Sun, Qingyu Yin, Hanqing Lu, Suhang Wang, Jingrui He, et al · 2024
Closest in time.
DiFashion: Towards Personalized Outfit Generation. In SIGIR
Yiyan Xu, Wenjie Wang, Fuli Feng, Yunshan Ma, Jizhi Zhang, and Xiangnan He. 2024 · 2024
Closest in time.
RA-Rec: An Efficient ID Representation Alignment Framework for LLM-based Recommendation
Xiaohan Yu, Li Zhang, Xin Zhao, Yue Wang, and Zhongrui Ma. 2024 · 2024
Closest in time.
Model-enhanced Vector Index
Hailin Zhang, Yujing Wang, Qi Chen, Ruiheng Chang, Ting Zhang, Ziming Miao, Yingyan Hou, Yang Ding, Xupeng Miao, Haonan Wang, et al · 2024
Closest in time.
Harnessing Large Language Models for Text-Rich Sequential Recommendation. In WWW . ACM
Zhi Zheng, Wenshuo Chao, Zhaopeng Qiu, Hengshu Zhu, and Hui Xiong. 2024 · 2024
Closest in time.