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Recommender Systems (RS) aim to provide personalized suggestions of items for users against consumer over-choice.
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We know what you want to buy: a demographic-based system for product recommendation on microblogs. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . 1935–1944
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Catastrophic forgetting in connectionist networks
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Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions
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Recommender system for online dating service
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The value of personalised recommender systems to e-business: a case study. In Proceedings of the 2008 ACM conference on Recommender systems . 291–294
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Knowledge discovery from data streams
João Gama, Jesús Aguilar-Ruiz, and Ralf Klinkenberg. 2008 · 2008
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Using context to improve predictive modeling of customers in personalization applications
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Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space
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The YouTube video recommendation system. In Proceedings of the fourth ACM conference on Recommender systems . 293–296
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Context-aware recommender systems
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Friendship and mobility: user movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining . 1082–1090
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Introduction to recommender systems handbook
Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011 · 2011
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Patterns of temporal variation in online media. In Proceedings of the fourth ACM international conference on Web search and data mining . 177–186
Jaewon Yang and Jure Leskovec. 2011 · 2011
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Comsoc: adaptive transfer of user behaviors over composite social network. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining . 696–704
Erheng Zhong, Wei Fan, Junwei Wang, Lei Xiao, and Yong Li. 2012 · 2012
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Terec: A temporal recommender system over tweet stream
Chen Chen, Hongzhi Yin, Junjie Yao, and Bin Cui. 2013 · 2013
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On evaluating stream learning algorithms
Joao Gama, Raquel Sebastiao, and Pedro Pereira Rodrigues. 2013 · 2013
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The plista dataset. In Proceedings of the 2013 international news recommender systems workshop and challenge . 16–23
Benjamin Kille, Frank Hopfgartner, Torben Brodt, and Tobias Heintz. 2013 · 2013
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Hidden factors and hidden topics: understanding rating dimensions with review text. In Proceedings of the 7th ACM conference on Recommender systems . 165–172
Julian McAuley and Jure Leskovec. 2013 · 2013
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Online multi-task collaborative filtering for on-the-fly recommender systems. In Proceedings of the 7th ACM conference on Recommender systems . 237–244
Jialei Wang, Steven CH Hoi, Peilin Zhao, and Zhi-Yong Liu. 2013 · 2013
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Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
Pedro G Campos, Fernando Díez, and Iván Cantador. 2014 · 2014
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Towards a dynamic top-n recommendation framework. In Proceedings of the 8th ACM Conference on Recommender Systems . 217–224
Xin Liu and Karl Aberer. 2014 · 2014
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Selective forgetting for incremental matrix factorization in recommender systems. In International conference on discovery science . Springer, 204–215
Pawel Matuszyk and Myra Spiliopoulou. 2014 · 2014
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Exploiting temporal influence in online recommendation. In Proceedings of the 8th ACM Conference on Recommender systems . 273–280
Róbert Pálovics, András A Benczúr, Levente Kocsis, Tamás Kiss, and Erzsébet Frigó. 2014 · 2014
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DeepWalk: Online Learning of Social Representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, New York, USA) (KDD ’14) . New York, NY, USA, 701–710
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014 · 2014
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xstreams: Recommending items to users with time-evolving preferences. In Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14) . 1–12
Zaigham Faraz Siddiqui, Eleftherios Tiakas, Panagiotis Symeonidis, Myra Spiliopoulou, and Yannis Manolopoulos. 2014 · 2014
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Sparse coding and lateral inhibition arising from balanced and unbalanced dendrodendritic excitation and inhibition
Yuguo Yu, Michele Migliore, Michael L Hines, and Gordon M Shepherd. 2014 · 2014
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Cross-domain recommender systems
Iván Cantador, Ignacio Fernández-Tobías, Shlomo Berkovsky, and Paolo Cremonesi. 2015 · 2015
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The netflix recommender system: Algorithms, business value, and innovation
Carlos A Gomez-Uribe and Neil Hunt. 2015 · 2015
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Evaluating recommender systems
Asela Gunawardana and Guy Shani. 2015 · 2015
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The movielens datasets: History and context
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
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Patrick J Laub, Thomas Taimre, and Philip K Pollett. 2015 · 2015
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Real-time recommendations for user-item streams. In Proceedings of the 30th Annual ACM Symposium on Applied Computing . 1039–1046
Andreas Lommatzsch and Sahin Albayrak. 2015 · 2015
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R. Srivastava, Klaus Greff, and J. Schmidhuber. 2015 · 2015
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30Music Listening and Playlists Dataset.. In RecSys Posters
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Evaluation of recommender systems in streaming environments
João Vinagre, Alípio Mário Jorge, and João Gama. 2015b · 2015
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Learning hierarchical representation model for nextbasket recommendation. In Proceedings of the 38th International ACM SIGIR conference on Research and Development in Information Retrieval . 403–412
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Connecting social media to e-commerce: Cold-start product recommendation using microblogging information
Wayne Xin Zhao, Sui Li, Yulan He, Edward Y Chang, Ji-Rong Wen, and Xiaoming Li. 2015 · 2015
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Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems . 7–10
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Coupled variational recurrent collaborative filtering. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 335–343
Qingquan Song, Shiyu Chang, and Xia Hu. 2019 · 2019
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DyRep: Learning Representations over Dynamic Graphs. In International Conference on Learning Representations
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Neural news recommendation with multi-head self-attention. 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) . 6389–6394
Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang, and Xing Xie. 2019b · 2019
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A simple convolutional generative network for next item recommendation. In Proceedings of the Twelfth 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
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Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems . 191–198
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Cited alongside, same era.
Deep coevolutionary network: Embedding user and item features for recommendation
Hanjun Dai, Yichen Wang, Rakshit Trivedi, and Le Song. 2016 · 2016
Cited alongside, same era.
Session-based Recommendations with Recurrent Neural Networks
Balázs Hidasi, Alexandros Karatzoglou, L. Baltrunas, and D. Tikk. 2016 · 2016
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Recommendations for streaming data. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management . 2185–2190
Karthik Subbian, Charu Aggarwal, and Kshiteesh Hegde. 2016 · 2016
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Streaming recommender systems. In Proceedings of the 26th international conference on world wide web . 381–389
Shiyu Chang, Yang Zhang, Jiliang Tang, Dawei Yin, Yi Chang, Mark A Hasegawa-Johnson, and Thomas S Huang. 2017 · 2017
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Flixster-dataset.zip
Tracy Dong. 2017 · 2017
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Online ranking prediction in non-stationary environments
Erzsébet Frigó, Róbert Pálovics, Domokos Kelen, Levente Kocsis, and András Benczúr. 2017 · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Dynamic News Recommendation with Hierarchical Attention Network. In 2019 IEEE International Conference on Data Mining (ICDM) . IEEE, 1456–1461
Hui Zhang, Xu Chen, and Shuai Ma. 2019a · 2019
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Deep learning based recommender system: A survey and new perspectives
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019b · 2019
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Joint optimization of tree-based index and deep model for recommender systems
Han Zhu, Daqing Chang, Ziru Xu, Pengye Zhang, Xiang Li, Jie He, Han Li, Jian Xu, and Kun Gai. 2019 · 2019
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Continual lifelong learning in natural language processing: A survey
Magdalena Biesialska, Katarzyna Biesialska, and Marta R Costa-jussà. 2020 · 2020
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Handling Information Loss of Graph Neural Networks for Session-based Recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1172–1180
Tianwen Chen and Raymond Chi-Wing Wong. 2020 · 2020
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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
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Time interval aware self-attention for sequential recommendation. In Proceedings of the 13th international conference on web search and data mining . 322–330
Jiacheng Li, Yujie Wang, and Julian McAuley. 2020 · 2020
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Memory augmented neural model for incremental session-based recommendation
Fei Mi and Boi Faltings. 2020 · 2020
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Ader: Adaptively distilled exemplar replay towards continual learning for session-based recommendation. In Fourteenth ACM Conference on Recommender Systems . 408–413
Fei Mi, Xiaoyu Lin, and Boi Faltings. 2020 · 2020
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Controlling fairness and bias in dynamic learning-to-rank. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 429–438
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DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks. In WSDM ’20: The Thirteenth ACM International Conference on Web Search and Data Mining . 519–527
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PA-GGAN: Session-Based Recommendation with Position-Aware Gated Graph Attention Network
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A Practical Incremental Method to Train Deep CTR Models
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Global Context Enhanced Graph Neural Networks for Session-based Recommendation
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Dynamic Graph Convolutional Networks for Entity Linking. In WWW ’20: The Web Conference . 1149–1159
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Graphsail: Graph structure aware incremental learning for recommender systems. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 2861–2868
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TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
Feng Yu, Yanqiao Zhu, Qiang Liu, S. Wu, L. Wang, and Tieniu Tan. 2020 · 2020
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How to retrain recommender system? A sequential meta-learning method. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 1479–1488
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A Survey on Stream-Based Recommender Systems
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Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 231–240
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LT-OCF: Learnable-Time ODE-based Collaborative Filtering. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 251–260
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A continual learning survey: Defying forgetting in classification tasks
Matthias Delange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Greg Slabaugh, and Tinne Tuytelaars. 2021 · 2021
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Continuous-time sequential recommendation with temporal graph collaborative transformer. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 433–442
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Locker: Locally Constrained Self-Attentive Sequential Recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 3088–3092
Zhankui He, Handong Zhao, Zhe Lin, Zhaowen Wang, Ajinkya Kale, and Julian Mcauley. 2021 · 2021
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Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems. In Fifteenth ACM Conference on Recommender Systems . 411–421
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Stratified and time-aware sampling based adaptive ensemble learning for streaming recommendations
Yan Zhao, Shoujin Wang, Yan Wang, and Hongwei Liu. 2021 · 2021
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Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management . 624–634
Jiayan Guo, Peiyan Zhang, Chaozhuo Li, Xing Xie, Yan Zhang, and Sunghun Kim. 2022 · 2022
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Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining . 168–176
Peiyan Zhang, Jiayan Guo, Chaozhuo Li, Yueqi Xie, Jae Boum Kim, Yan Zhang, Xing Xie, Haohan Wang, and Sunghun Kim. 2023 · 2023
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