Fetching the paper…
Reading the bibliography…
Current multimodal sequential recommendation models are often unable to effectively explore and capture correlations among behavior sequences of users and items across different modalities, either neglecting correlations among sequence representations or inadequately capturing associations between multimodal data and sequence data in their representations.
S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 1893–1902
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020b · 1902
Earlier work this paper cites.
Why does unsupervised pre-training help deep learning?. In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics . 201–208
Dumitru Erhan, Aaron Courville, Yoshua Bengio, and Pascal Vincent. 2010 · 2010
Earlier work this paper cites.
Factorizing Personalized Markov Chains for Next-Basket Recommendation. In Proceedings of the Web Conference 2010 . 811–820
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010 · 2010
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations
Diederik P Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of the AAAI Conference on Artificial Intelligence
Ruining He and Julian McAuley. 2016 · 2016
Earlier work this paper cites.
Sequential User-based Recurrent Neural Network Recommendations. In Proceedings of the 11th ACM Conference on Recommender Systems . 152–160
Tim Donkers, Benedikt Loepp, and Jürgen Ziegler. 2017 · 2017
Earlier work this paper cites.
Visually-Aware Fashion Recommendation and Design with Generative Image Models. In 2017 IEEE International Conference on Data Mining (ICDM) . IEEE, 207–216
Wang-Cheng Kang, Chen Fang, Zhaowen Wang, and Julian McAuley. 2017 · 2017
Earlier work this paper cites.
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. In International Conference on Learning Representations
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017 · 2017
Earlier work this paper cites.
Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation
Qiang Cui, Shu Wu, Qiang Liu, Wen Zhong, and Liang Wang. 2018 · 2018
Earlier work this paper cites.
Self-Attentive Sequential Recommendation. In 2018 IEEE International Conference on Data Mining (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 Proceedings of the 11th ACM International Conference on Web Search and Data Mining . 565–573
Jiaxi Tang and Ke Wang. 2018 · 2018
Earlier work this paper cites.
Item recommendation on monotonic behavior chains. In Proceedings of the 12th ACM Conference on Recommender Systems, RecSys 2018, Vancouver, BC, Canada, October 2-7, 2018 , Sole Pera, Michael D. Ekstrand, Xavier Amatriain, and John O’Donovan (Eds.). ACM, 86–94
Mengting Wan and Julian J. McAuley. 2018 · 2018
Earlier work this paper cites.
Graphcar: Content-aware multimedia recommendation with graph autoencoder. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . 981–984
Qidi Xu, Fumin Shen, Li Liu, and Heng Tao Shen. 2018 · 2018
Earlier work this paper cites.
User-Video Co-Attention Network for Personalized Micro-video Recommendation. In Proceedings of the Web Conference 2019 . 3020–3026
Shang Liu, Zhenzhong Chen, Hongyi Liu, and Xinghai Hu. 2019a · 2019
Earlier work this paper cites.
Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019 · 2019
Earlier work this paper cites.
Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . 188–197
Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019 · 2019
Earlier work this paper cites.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Earlier work this paper cites.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . 3982–3992
Nils Reimers and Iryna Gurevych. 2019 · 2019
Earlier work this paper cites.
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. In Proceedings of the 28th ACM International Conference on Information & Knowledge Management . 1441–1450
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 2019
Earlier work this paper cites.
Fine-Grained Spoiler Detection from Large-Scale Review Corpora. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers , Anna Korhonen, David R. Traum, and Lluís Màrquez (Eds.). Association for Computational Linguistics, 2605–2610
Mengting Wan, Rishabh Misra, Ndapa Nakashole, and Julian J. McAuley. 2019 · 2019
Earlier work this paper cites.
Sequential Recommender Systems: Challenges, Progress and Prospects. In Proceedings of the 28th International Joint Conference on Artificial Intelligence . 6332–6338
S Wang, L Hu, Y Wang, L Cao, QZ Sheng, and M Orgun. 2019 · 2019
Cited alongside, same era.
MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video. In Proceedings of the 27th ACM International Conference on Multimedia . 1437–1445
Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, and Tat-Seng Chua. 2019 · 2019
Cited alongside, same era.
A Simple Convolutional Generative Network for Next Item Recommendation. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining . 582–590
Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M Jose, and Xiangnan He. 2019 · 2019
Cited alongside, same era.
Feature-level Deeper Self-Attention Network for Sequential Recommendation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence . 4320–4326
Tingting Zhang, Pengpeng Zhao, Yanchi Liu, Victor S Sheng, Jiajie Xu, Deqing Wang, Guanfeng Liu, and Xiaofang Zhou. 2019 · 2019
Cited alongside, same era.
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 4653–4664
Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Yushuo Chen, Xingyu Pan, Kaiyuan Li, Yujie Lu, Hui Wang, Changxin Tian, et al · 2021
Later among the works it cites.
Vlmo: Unified vision-language pre-training with mixture-of-modality-experts
Hangbo Bao, Wenhui Wang, Li Dong, Qiang Liu, Owais Khan Mohammed, Kriti Aggarwal, Subhojit Som, Songhao Piao, and Furu Wei. 2022 · 2022
Later among the works it cites.
Towards Universal Sequence Representation Learning for Recommender Systems. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 585–593
Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, and Ji-Rong Wen. 2022 · 2022
Later among the works it cites.
Memory Bank Augmented Long-tail Sequential Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management . 791–801
Yidan Hu, Yong Liu, Chunyan Miao, and Yuan Miao. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 639–648
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
Cited alongside, same era.
Vl-bert: Pre-training of generic visual-linguistic representations. In International Conference on Learning Representations
Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. 2020 · 2020
Cited alongside, same era.
Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback. In Proceedings of the 28th ACM International Conference on Multimedia . 3541–3549
Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, and Tat-Seng Chua. 2020 · 2020
Cited alongside, same era.
Sequential Recommendation with Graph Neural Networks. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 378–387
Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin, and Yong Li. 2021 · 2021
Cited alongside, same era.
Learning transferable user representations with sequential behaviors via contrastive pre-training. In 2021 IEEE International Conference on Data Mining (ICDM) . IEEE, 51–60
Mingyue Cheng, Fajie Yuan, Qi Liu, Xin Xin, and Enhong Chen. 2021 · 2021
Cited alongside, same era.
Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning . PMLR, 4904–4916
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. 2021 · 2021
Cited alongside, same era.
Vilt: Vision-and-language transformer without convolution or region supervision. In International Conference on Machine Learning . PMLR, 5583–5594
Wonjae Kim, Bokyung Son, and Ildoo Kim. 2021 · 2021
Cited alongside, same era.
Semi: A sequential multi-modal information transfer network for e-commerce micro-video recommendations. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 3161–3171
Chenyi Lei, Yong Liu, Lingzi Zhang, Guoxin Wang, Haihong Tang, Houqiang Li, and Chunyan Miao. 2021 · 2021
Cited alongside, same era.
Towards Fast Adaptation of Pretrained Contrastive Models for Multi-channel Video-Language Retrieval
Xudong Lin, Simran Tiwari, Shiyuan Huang, Manling Li, Mike Zheng Shou, Heng Ji, and Shih-Fu Chang. 2022 · 2022
Later among the works it cites.
Multimodal Meta-Learning for Cold-Start Sequential Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management . 3421–3430
Xingyu Pan, Yushuo Chen, Changxin Tian, Zihan Lin, Jinpeng Wang, He Hu, and Wayne Xin Zhao. 2022 · 2022
Later among the works it cites.
A review-aware graph contrastive learning framework for recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1283–1293
Jie Shuai, Kun Zhang, Le Wu, Peijie Sun, Richang Hong, Meng Wang, and Yong Li. 2022 · 2022
Later among the works it cites.
TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback
Jie Wang, Fajie Yuan, Mingyue Cheng, Joemon M Jose, Chenyun Yu, Beibei Kong, Zhijin Wang, Bo Hu, and Zang Li. 2022c · 2022
Later among the works it cites.
Image as a foreign language: Beit pretraining for all vision and vision-language tasks
Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed, Saksham Singhal, Subhojit Som, et al · 2022
Later among the works it cites.
Personalized prompts for sequential recommendation
Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xu Zhang, Leyu Lin, and Qing He. 2022 · 2022
Later among the works it cites.
Contrastive learning for sequential recommendation. In 2022 IEEE 38th International Conference on Data Engineering (ICDE) . 1259–1273
Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022a · 2022
Later among the works it cites.
Adaptive multi-modalities fusion in sequential recommendation systems. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management . 843–853
Hengchang Hu, Wei Guo, Yong Liu, and Min-Yen Kan. 2023 · 2023
Closest in time.
MMMLP: multi-modal multilayer perceptron for sequential recommendations. In Proceedings of the ACM Web Conference 2023 . 1109–1117
Jiahao Liang, Xiangyu Zhao, Muyang Li, Zijian Zhang, Wanyu Wang, Haochen Liu, and Zitao Liu. 2023 · 2023
Closest in time.
Where to go next for recommender systems? id-vs. modality-based recommender models revisited. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2639–2649
Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. 2023 · 2023
Closest in time.
Hongyu Zhou, Xin Zhou, Zhiwei Zeng, Lingzi Zhang, and Zhiqi Shen. 2023b · 2023
Closest in time.
Enhancing dyadic relations with homogeneous graphs for multimodal recommendation
Hongyu Zhou, Xin Zhou, Lingzi Zhang, and Zhiqi Shen. 2023c · 2023
Closest in time.
A tale of two graphs: Freezing and denoising graph structures for multimodal recommendation. In Proceedings of the 31st ACM International Conference on Multimedia . 935–943
Xin Zhou and Zhiqi Shen. 2023 · 2023
Closest in time.
Bootstrap latent representations for multi-modal recommendation. In Proceedings of the ACM Web Conference 2023 . 845–854
Xin Zhou, Hongyu Zhou, Yong Liu, Zhiwei Zeng, Chunyan Miao, Pengwei Wang, Yuan You, and Feijun Jiang. 2023a · 2023
Closest in time.
GreenRec: A Large-Scale Dataset for Green Food Recommendation. In Companion Proceedings of the ACM on Web Conference 2024 . 625–628
Lingzi Zhang, Yinan Zhang, Xin Zhou, and Zhiqi Shen. 2024a · 2024
Closest in time.
Are ID Embeddings Necessary? Whitening Pre-trained Text Embeddings for Effective Sequential Recommendation. In 2024 IEEE 40th International Conference on Data Engineering (ICDE)
Lingzi Zhang, Xin Zhou, Zhiwei Zeng, and Zhiqi Shen. 2024b · 2024
Closest in time.
Graph Contrastive Learning with Adaptive Augmentation. In Proceedings of the Web Conference 2021 . 2069–2080
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2021 · 2080
Closest in time.