research-article

Open access

Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights

Authors:

Shenghui Cheng,

Yunzhu PanAuthors Info & Claims

WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining

Pages 208 - 217

https://doi.org/10.1145/3616855.3635805

Published: 04 March 2024 Publication History

Abstract

Adapters, a plug-in neural network module with some tunable parameters, have emerged as a parameter-efficient transfer learning technique for adapting pre-trained models to downstream tasks, especially for natural language processing (NLP) and computer vision (CV) fields. Meanwhile, learning recommendation models directly from raw item modality features --- e.g., texts of NLP and images of CV --- can enable effective and transferable recommender systems (called TransRec). In view of this, a natural question arises:can adapter-based learning techniques achieve parameter-efficient TransRec with good performance?

To this end, we perform empirical studies to address several key sub-questions. First, we ask whether the adapter-based TransRec performs comparably to TransRec based on standard full-parameter fine-tuning? does it hold for recommendation with different item modalities, e.g., textual RS and visual RS. If yes, we benchmark these existing adapters, which have been shown to be effective in NLP and CV tasks, in item recommendation tasks. Third, we carefully study several key factors for the adapter-based TransRec in terms of where and how to insert these adapters? Finally, we look at the effects of adapter-based TransRec by either scaling up its source training data or scaling down its target training data. Our paper provides key insights and practical guidance on unified & transferable recommendation --- a less studied recommendation scenario. We release our codes and other materials at https://github.com/westlake-repl/Adapter4Rec/.

References

[1]

Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023. Tallrec: An effective and efficient tuning framework to align large language model with recommendation. arXiv preprint arXiv:2305.00447 (2023).

[2]

Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021).

[3]

Tom Brown et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877--1901.

[4]

Guanzheng Chen, Fangyu Liu, Zaiqiao Meng, and Shangsong Liang. 2022b. Revisiting parameter-efficient tuning: Are we really there yet? arXiv preprint arXiv:2202.07962 (2022).

[5]

Zhe Chen, Yuchen Duan, Wenhai Wang, Junjun He, Tong Lu, Jifeng Dai, and Yu Qiao. 2022a. Vision transformer adapter for dense predictions. arXiv preprint arXiv:2205.08534 (2022).

[6]

Yu Cheng, Yunzhu Pan, Jiaqi Zhang, Yongxin Ni, Aixin Sun, and Fajie Yuan. 2023. An Image Dataset for Benchmarking Recommender Systems with Raw Pixels. arXiv preprint arXiv:2309.06789 (2023).

[7]

Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. 2022. M6-rec: Generative pretrained language models are open-ended recommender systems. arXiv preprint arXiv:2205.08084 (2022).

[8]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).

[9]

Hao Ding, Yifei Ma, Anoop Deoras, Yuyang Wang, and Hao Wang. 2021. Zero-shot recommender systems. arXiv preprint arXiv:2105.08318 (2021).

[10]

Alexey Dosovitskiy, Lucas Beyer, et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).

[11]

Peng Gao, Jiaming Han, Renrui Zhang, Ziyi Lin, Shijie Geng, Aojun Zhou, Wei Zhang, Pan Lu, Conghui He, Xiangyu Yue, et al. 2023. Llama-adapter v2: Parameter-efficient visual instruction model. arXiv preprint arXiv:2304.15010 (2023).

[12]

Xuri Ge, Joemon M Jose, Pengcheng Wang, Arunachalam Iyer, Xiao Liu, and Hu Han. 2023. ALGRNet: Multi-Relational Adaptive Facial Action Unit Modelling for Face Representation and Relevant Recognitions. IEEE Transactions on Biometrics, Behavior, and Identity Science (2023).

[13]

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). arXiv preprint arXiv:2203.13366 (2022).

[14]

Shijie Geng, Juntao Tan, Shuchang Liu, Zuohui Fu, and Yongfeng Zhang. 2023. VIP5: Towards Multimodal Foundation Models for Recommendation. arXiv preprint arXiv:2305.14302 (2023).

[15]

Jon Atle Gulla, Lemei Zhang, Peng Liu, Özlem Özgöbek, and Xiaomeng Su. 2017. The adressa dataset for news recommendation. In Proceedings of the international conference on web intelligence. 1042--1048.

Digital Library

[16]

Wenjuan Han, Bo Pang, and Yingnian Wu. 2021. Robust transfer learning with pretrained language models through adapters. arXiv preprint arXiv:2108.02340 (2021).

[17]

Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. 2022a. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 16000--16009.

[18]

Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In proceedings of the 25th international conference on world wide web. 507--517.

Digital Library

[19]

Xuehai He, Chunyuan Li, Pengchuan Zhang, Jianwei Yang, and Xin Eric Wang. 2022b. Parameter-efficient Fine-tuning for Vision Transformers. https://doi.org/10.48550/ARXIV.2203.16329

[20]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 173--182.

Digital Library

[21]

Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, and Ji-Rong Wen. 2022. Towards Universal Sequence Representation Learning for Recommender Systems. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 585--593.

Digital Library

[22]

Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for NLP. In International Conference on Machine Learning. PMLR, 2790--2799.

[23]

Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021).

[24]

Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim. 2022. Visual prompt tuning. In European Conference on Computer Vision. Springer, 709--727.

Digital Library

[25]

Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM). IEEE, 197--206.

[26]

Rabeeh Karimi Mahabadi, James Henderson, and Sebastian Ruder. 2021. Compacter: Efficient low-rank hypercomplex adapter layers. Advances in Neural Information Processing Systems 34 (2021), 1022--1035.

[27]

Walid Krichene and Steffen Rendle. 2020. On Sampled Metrics for Item Recommendation. In KDD.

[28]

Brian Lester, Rami Al-Rfou, and Noah Constant. 2021. The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691 (2021).

[29]

Bin Li, Qiang Yang, and Xiangyang Xue. 2009. Transfer learning for collaborative filtering via a rating-matrix generative model. In Proceedings of the 26th annual international conference on machine learning. 617--624.

Digital Library

[30]

Lei Li, Yongfeng Zhang, and Li Chen. 2022. Personalized prompt learning for explainable recommendation. arXiv preprint arXiv:2202.07371 (2022).

[31]

Pan Li, Yuyan Wang, Ed H Chi, and Minmin Chen. 2023 b. Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations. arXiv preprint arXiv:2306.01475 (2023).

[32]

Ruyu Li, Wenhao Deng, Yu Cheng, Zheng Yuan, Jiaqi Zhang, and Fajie Yuan. 2023 a. Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights. arXiv preprint arXiv:2305.11700 (2023).

[33]

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019).

[34]

Yuting Liu, Enneng Yang, Yizhou Dang, Guibing Guo, Qiang Liu, Yuliang Liang, Linying Jiang, and Xingwei Wang. 2023. ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation. arXiv preprint arXiv:2311.05956 (2023).

[35]

Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021. Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 10012--10022.

[36]

Fang Ma, Chen Zhang, Lei Ren, Jingang Wang, Qifan Wang, Wei Wu, Xiaojun Quan, and Dawei Song. 2022b. XPrompt: Exploring the Extreme of Prompt Tuning. arXiv preprint arXiv:2210.04457 (2022).

[37]

Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018. 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. 1930--1939.

Digital Library

[38]

Xinyu Ma, Jiafeng Guo, Ruqing Zhang, Yixing Fan, and Xueqi Cheng. 2022a. Scattered or Connected? An Optimized Parameter-efficient Tuning Approach for Information Retrieval. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 1471--1480.

Digital Library

[39]

Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. 43--52.

Digital Library

[40]

Yongxin Ni, Yu Cheng, Xiangyan Liu, Junchen Fu, Youhua Li, Xiangnan He, Yongfeng Zhang, and Fajie Yuan. 2023. A Content-Driven Micro-Video Recommendation Dataset at Scale. arXiv preprint arXiv:2309.15379 (2023).

[41]

Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018).

[42]

Weike Pan, Nathan N Liu, Evan W Xiang, and Qiang Yang. 2011. Transfer learning to predict missing ratings via heterogeneous user feedbacks. In Twenty-Second International Joint Conference on Artificial Intelligence.

[43]

Weike Pan, Evan Xiang, Nathan Liu, and Qiang Yang. 2010. Transfer learning in collaborative filtering for sparsity reduction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 24. 230--235.

[44]

Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vulić, Sebastian Ruder, Kyunghyun Cho, and Iryna Gurevych. 2020. Adapterhub: A framework for adapting transformers. arXiv preprint arXiv:2007.07779 (2020).

[45]

Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, and Sebastian Ruder. 2020. Mad-x: An adapter-based framework for multi-task cross-lingual transfer. arXiv preprint arXiv:2005.00052 (2020).

[46]

Can Qin, Sungchul Kim, Handong Zhao, Tong Yu, Ryan A Rossi, and Yun Fu. 2022. External Knowledge Infusion for Tabular Pre-training Models with Dual-adapters. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1401--1409.

Digital Library

[47]

Zekai Qu, Ruobing Xie, Chaojun Xiao, Yuan Yao, Zhiyuan Liu, Fengzong Lian, Zhanhui Kang, and Jie Zhou. 2023. Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language. arXiv preprint arXiv:2310.13540 (2023).

[48]

Alec Radford, Jong Wook Kim, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PMLR, 8748--8763.

[49]

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. Recommender Systems with Generative Retrieval. arXiv preprint arXiv:2305.05065 (2023).

[50]

Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng, et al. 2021. 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. 4104--4113.

Digital Library

[51]

Kyuyong Shin, Hanock Kwak, Kyung-Min Kim, Minkyu Kim, Young-Jin Park, Jisu Jeong, and Seungjae Jung. 2021b. One4all user representation for recommender systems in e-commerce. arXiv preprint arXiv:2106.00573 (2021).

[52]

Kyuyong Shin, Hanock Kwak, Kyung-Min Kim, Su Young Kim, and Max Nihlen Ramstrom. 2021a. Scaling Law for Recommendation Models: Towards General-purpose User Representations. arXiv preprint arXiv:2111.11294 (2021).

[53]

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.

Digital Library

[54]

Rui Sun, Xuezhi Cao, Yan Zhao, Junchen Wan, Kun Zhou, Fuzheng Zhang, Zhongyuan Wang, and Kai Zheng. 2020. Multi-modal knowledge graphs for recommender systems. In Proceedings of the 29th ACM international conference on information & knowledge management. 1405--1414.

Digital Library

[55]

Yi-Lin Sung, Jaemin Cho, and Mohit Bansal. 2022. VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5227--5237.

[56]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).

[57]

Jie Wang, Fajie Yuan, Mingyue Cheng, Joemon M Jose, Chenyun Yu, Beibei Kong, Xiangnan He, Zhijin Wang, Bo Hu, and Zang Li. 2022a. Transrec: Learning transferable recommendation from mixture-of-modality feedback. arXiv preprint arXiv:2206.06190 (2022).

[58]

Jinpeng Wang, Ziyun Zeng, Yunxiao Wang, Yuting Wang, Xingyu Lu, Tianxiang Li, Jun Yuan, Rui Zhang, Hai-Tao Zheng, and Shu-Tao Xia. 2023. MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation. In Proceedings of the 31st ACM International Conference on Multimedia. 6548--6557.

Digital Library

[59]

Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Guihong Cao, Daxin Jiang, Ming Zhou, et al. 2020. K-adapter: Infusing knowledge into pre-trained models with adapters. arXiv preprint arXiv:2002.01808 (2020).

[60]

Xiaolei Wang, Kun Zhou, Ji-Rong Wen, and Wayne Xin Zhao. 2022b. Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1929--1937.

Digital Library

[61]

Wei Wei, Chao Huang, Lianghao Xia, and Chuxu Zhang. 2023. Multi-Modal Self-Supervised Learning for Recommendation. In Proceedings of the ACM Web Conference 2023. 790--800.

Digital Library

[62]

Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. 2021a. 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.

Digital Library

[63]

Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. 2021b. Mm-rec: multimodal news recommendation. arXiv preprint arXiv:2104.07407 (2021).

[64]

Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. 2021c. Two birds with one stone: Unified model learning for both recall and ranking in news recommendation. arXiv preprint arXiv:2104.07404 (2021).

[65]

Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. 2022a. End-to-end Learnable Diversity-aware News Recommendation. arXiv preprint arXiv:2204.00539 (2022).

[66]

Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, et al. 2020. Mind: A large-scale dataset for news recommendation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 3597--3606.

[67]

Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Ao Xiang, Xu Zhang, Leyu Lin, and Qing He. 2022b. Selective fairness in recommendation via prompts. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2657--2662.

Digital Library

[68]

Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xu Zhang, Leyu Lin, and Qing He. 2022c. Personalized Prompts for Sequential Recommendation. arXiv preprint arXiv:2205.09666 (2022).

[69]

Shitao Xiao, Zheng Liu, Yingxia Shao, Tao Di, Bhuvan Middha, Fangzhao Wu, and Xing Xie. 2022. Training large-scale news recommenders with pretrained language models in the loop. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4215--4225.

Digital Library

[70]

Xin Xin, Tiago Pimentel, Alexandros Karatzoglou, Pengjie Ren, Konstantina Christakopoulou, and Zhaochun Ren. 2022. Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1347--1357.

Digital Library

[71]

Shenghao Yang, Chenyang Wang, Yankai Liu, Kangping Xu, Weizhi Ma, Yiqun Liu, Min Zhang, Haitao Zeng, Junlan Feng, and Chao Deng. 2023. Collaborative Word-based Pre-trained Item Representation for Transferable Recommendation. arXiv preprint arXiv:2311.10501 (2023).

[72]

Fajie Yuan, Xiangnan He, Alexandros Karatzoglou, and Liguang Zhang. 2020. Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation. arXiv e-prints (2020), arXiv--2001.

[73]

Fajie Yuan, Guoxiao Zhang, Alexandros Karatzoglou, Joemon Jose, Beibei Kong, and Yudong Li. 2021. One person, one model, one world: Learning continual user representation without forgetting. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 696--705.

Digital Library

[74]

Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. 2023. Where to go next for recommender systems? id-vs. modality-based recommender models revisited. arXiv preprint arXiv:2303.13835 (2023).

[75]

Jiaqi Zhang, Yu Cheng, Yongxin Ni, Yunzhu Pan, Zheng Yuan, Junchen Fu, Youhua Li, Jie Wang, and Fajie Yuan. 2023 a. NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation. arXiv preprint arXiv:2309.07705 (2023).

[76]

Renrui Zhang, Jiaming Han, Aojun Zhou, Xiangfei Hu, Shilin Yan, Pan Lu, Hongsheng Li, Peng Gao, and Yu Qiao. 2023 b. Llama-adapter: Efficient fine-tuning of language models with zero-init attention. arXiv preprint arXiv:2303.16199 (2023).

[77]

Zizhuo Zhang and Bang Wang. 2023. Prompt learning for news recommendation. arXiv preprint arXiv:2304.05263 (2023).

[78]

Yaoming Zhu, Jiangtao Feng, Chengqi Zhao, Mingxuan Wang, and Lei Li. 2021. Serial or parallel? plug-able adapter for multilingual machine translation. arXiv preprint arXiv:2104.08154 6, 3 (2021).

Cited By

Liu QZhu JYang YDai QDu ZWu XZhao ZZhang RDong ZBaeza-Yates RBonchi F(2024)Multimodal Pretraining, Adaptation, and Generation for Recommendation: A SurveyProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671473(6566-6576)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671473
Wang JKaratzoglou AArapakis IJose JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action ModelingProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657767(375-385)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657767
Fu JGe XXin XKaratzoglou AArapakis IWang JJose JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFTProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657725(687-697)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657725
Show More Cited By

Index Terms

Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Recommending Serendipitous Items using Transfer Learning
CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Most recommender algorithms are designed to suggest relevant items, but suggesting these items does not always result in user satisfaction. Therefore, the efforts in recommender systems recently shifted towards serendipity, but generating serendipitous ...
Improving Accuracy of Recommender System by Item Clustering

Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...
The Application of Transfer Learning on E-Commerce Recommender Systems
WISA '13: Proceedings of the 2013 10th Web Information System and Application Conference

Nowadays, recommender systems occupy an increasingly important position in people's lives. Recommender systems are widely applied in e-commerce websites, they discover users' potential consuming habits by analyzing their behaviors, and then recommend ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining

March 2024

1246 pages

ISBN:9798400703713

DOI:10.1145/3616855

General Chairs:
Luz Angélica
Caudillo Mata (MDA Geointelligence)
,
Silvio Lattanzi
Google Research
,
Andrés Muñoz Medina
Google Research
,
Program Chairs:
Leman Akoglu
CMU
,
Aristides Gionis
KTH
,
Sergei Vassilvitskii
Google Research

Copyright © 2024 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 March 2024

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WSDM '24

Sponsor:

WSDM '24: The 17th ACM International Conference on Web Search and Data Mining

March 4 - 8, 2024

Merida, Mexico

Acceptance Rates

Overall Acceptance Rate 498 of 2,863 submissions, 17%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
1,130
Total Downloads

Downloads (Last 12 months)1,130
Downloads (Last 6 weeks)191

Reflects downloads up to 14 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Liu QZhu JYang YDai QDu ZWu XZhao ZZhang RDong ZBaeza-Yates RBonchi F(2024)Multimodal Pretraining, Adaptation, and Generation for Recommendation: A SurveyProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671473(6566-6576)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671473
Wang JKaratzoglou AArapakis IJose JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action ModelingProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657767(375-385)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657767
Fu JGe XXin XKaratzoglou AArapakis IWang JJose JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFTProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657725(687-697)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657725
Li YDu HNi YZhao PGuo QYuan FZhou X(2024)Multi-Modality is All You Need for Transferable Recommender Systems2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00380(5008-5021)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00380
Wu LZheng ZQiu ZWang HGu HShen TQin CZhu CZhu HLiu QXiong HChen E(2024)A survey on large language models for recommendationWorld Wide Web10.1007/s11280-024-01291-227:5Online publication date: 22-Aug-2024
https://doi.org/10.1007/s11280-024-01291-2
Chen JLiu ZHuang XWu CLiu QJiang GPu YLei YChen XWang XZheng KLian DChen E(2024)When large language models meet personalization: perspectives of challenges and opportunitiesWorld Wide Web10.1007/s11280-024-01276-127:4Online publication date: 28-Jun-2024
https://doi.org/10.1007/s11280-024-01276-1

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents