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Sequential Recommender Systems (SRSs) are a popular type of recommender system that learns from a user's history to predict the next item they are likely to interact with.
E-commerce recommendation applications
J Ben Schafer, Joseph A Konstan, and John Riedl. 2001 · 2001
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Search shortcuts: a new approach to the recommendation of queries
Ranieri Baraglia, Fidel Cacheda, Victor Carneiro, Diego Fernandez, Vreixo Formoso, Raffaele Perego, and Fabrizio Silvestri. 2009 · 2009
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Context-aware recommender systems
Gediminas Adomavicius and Alexander Tuzhilin. 2010 · 2010
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Introduction to recommender systems handbook
Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011 · 2011
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Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs
Dingqi Yang, Daqing Zhang, Vincent W Zheng, and Zhiyong Yu. 2014 · 2014
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Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015 · 2015
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Music recommender systems
Markus Schedl, Peter Knees, Brian McFee, Dmitry Bogdanov, and Marius Kaminskas. 2015 · 2015
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Parallel recurrent neural network architectures for feature-rich session-based recommendations
Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016 · 2016
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Neural attentive session-based recommendation
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017 · 2017
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Recurrent neural networks with top-k gains for session-based recommendations
Balázs Hidasi and Alexandros Karatzoglou. 2018 · 2018
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Recommendation system development for fashion retail e-commerce
Hyunwoo Hwangbo, Yang Sok Kim, and Kyung Jin Cha. 2018 · 2018
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Self-attentive sequential recommendation
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Cited alongside, same era.
Sequence-aware recommender systems
Massimo Quadrana, Paolo Cremonesi, and Dietmar Jannach. 2018 · 2018
Cited alongside, same era.
Current challenges and visions in music recommender systems research
Markus Schedl, Hamed Zamani, Ching-Wei Chen, Yashar Deldjoo, and Mehdi Elahi. 2018 · 2018
Cited alongside, same era.
Personalized top-n sequential recommendation via convolutional sequence embedding
Jiaxi Tang and Ke Wang. 2018 · 2018
Cited alongside, same era.
A LSTM based model for personalized context-aware citation recommendation
Libin Yang, Yu Zheng, Xiaoyan Cai, Hang Dai, Dejun Mu, Lantian Guo, and Tao Dai. 2018 · 2018
Cited alongside, same era.
Atrank: An attention-based user behavior modeling framework for recommendation
Chang Zhou, Jinze Bai, Junshuai Song, Xiaofei Liu, Zhengchao Zhao, Xiusi Chen, and Jun Gao. 2018 · 2018
Time interval aware self-attention for sequential recommendation
Jiacheng Li, Yujie Wang, and Julian McAuley. 2020 · 2020
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Adversarial training for session-based item recommendations
Duo Liu, Yang Sun, Xiaoyan Zhao, Gengxiang Zhang, and Rui Liu. 2020 · 2020
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Sequential recommendation with graph neural networks
Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin, and Yong Li. 2021 · 2021
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Continuous-time sequential recommendation with temporal graph collaborative transformer
Ziwei Fan, Zhiwei Liu, Jiawei Zhang, Yun Xiong, Lei Zheng, and Philip S Yu. 2021 · 2021
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Denoising implicit feedback for recommendation
Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, and Tat-Seng Chua. 2021 · 2021
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Rank List Sensitivity of Recommender Systems to Interaction Perturbations
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Cited alongside, same era.
Dealing with noise problem in machine learning data-sets: A systematic review
Shivani Gupta and Atul Gupta. 2019 · 2019
Cited alongside, same era.
BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 2019
Cited alongside, same era.
Deep learning based recommender system: A survey and new perspectives
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019 · 2019
Cited alongside, same era.
A review of movie recommendation system: Limitations, Survey and Challenges
Mahesh Goyani and Neha Chaurasiya. 2020 · 2020
Cited alongside, same era.
Context Aware Recommendation Systems: A review of the state of the art techniques
Saurabh Kulkarni and Sunil F Rodd. 2020 · 2020
Cited alongside, same era.
Web Recommendation System Based on a Markov-Chainmodel
Francois Fouss, Stephane Faulkner, Manuel Kolp, Alain Pirotte, Marco Saerens, et al
Cited in the paper.
Sejoon Oh, Berk Ustun, Julian McAuley, and Srijan Kumar. 2022 · 2022
Later among the works it cites.
Effective and Efficient Training for Sequential Recommendation using Recency Sampling. In Proceedings of the 16th ACM Conference on Recommender Systems . 81–91
Aleksandr Petrov and Craig Macdonald. 2022 · 2022
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From counter-intuitive observations to a fresh look at recommender system
Aixin Sun. 2022 · 2022
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A revisiting study of appropriate offline evaluation for top-N recommendation algorithms
Wayne Xin Zhao, Zihan Lin, Zhichao Feng, Pengfei Wang, and Ji-Rong Wen. 2022 · 2022
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A critical study on data leakage in recommender system offline evaluation
Yitong Ji, Aixin Sun, Jie Zhang, and Chenliang Li. 2023 · 2023
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