Transactions on Recommender Systems, Volume 3 (original) (raw)

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Volume 3, Number 1, March 2025

Highlights of RecSys '22

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    Aleksandr V. Petrov, Craig Macdonald:
    RSS: Effective and Efficient Training for Sequential Recommendation Using Recency Sampling. 1:1-1:32
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    Simone Borg Bruun, Christina Lioma, Maria Maistro:
    Recommending Target Actions Outside Sessions in the Data-poor Insurance Domain. 2:1-2:24
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    Dilina Chandika Rajapakse, Douglas J. Leith:
    User Cold-Start Learning in Recommender Systems using Monte Carlo Tree Search. 3:1-3:23
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    Maryam Aziz, Jesse Anderton, Kevin Jamieson, Alice Wang, Hugues Bouchard, Javed A. Aslam:
    Unbiased Identification of Broadly Appealing Content Using a Pure Exploration Infinitely Armed Bandit Strategy. 4:1-4:22
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    Sanne Vrijenhoek, Gabriel Bénédict, Mateo Gutierrez Granada, Daan Odijk:
    RADio* - An Introduction to Measuring Normative Diversity in News Recommendations. 5:1-5:29
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    Ori Katz, Oren Barkan, Noam Koenigstein:
    Personalized Cadence Awareness for Next Basket Recommendation. 6:1-6:23
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    Shuyang Li, Bodhisattwa Prasad Majumder, Julian J. McAuley:
    Self-Supervised Bot Play for Transcript-Free Conversational Critiquing with Rationales. 7:1-7:20
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    Costas Panagiotakis, Evangelia Daskalaki, Harris Papadakis, Paraskevi Fragopoulou:
    An Expectation-Maximization framework for Personalized Itinerary Recommendation with POI Categories and Must-see POIs. 1-33
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    Shuyuan Xu, Da Xu, Evren Körpeoglu, Sushant Kumar, Stephen Guo, Kannan Achan, Yongfeng Zhang:
    Causal Structure Learning for Recommender System. 1-23
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    Lingzi Zhang, Xin Zhou, Zhiwei Zeng, Zhiqi Shen:
    Multimodal Pre-training for Sequential Recommendation via Contrastive Learning. 1-23
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    Youru Li, Zhenfeng Zhu, Shaohu Chen, Kaiming Shen, Xingxing Zhang, Wenliang Zhong, Yao Zhao:
    Determinantal Point Processes Guided Crowd-wise Mixture-of-Experts for Recommendation in Alipay. 1-15

Volume 3, Number 2, June 2025

Special Issue on Trustworthy Recommender Systems

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    Markus Schedl, Yashar Deldjoo, Pablo Castells, Emine Yilmaz:
    Introduction to the Special Issue on Trustworthy Recommender Systems. 12:1-12:8
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    Yingqiang Ge, Shuchang Liu, Zuohui Fu, Juntao Tan, Zelong Li, Shuyuan Xu, Yunqi Li, Yikun Xian, Yongfeng Zhang:
    A Survey on Trustworthy Recommender Systems. 13:1-13:68
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    David Neumann, Andreas Lutz, Karsten Müller, Wojciech Samek:
    A Privacy Preserving System for Movie Recommendations Using Federated Learning. 14:1-14:51
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    Sulthana Shams, Douglas J. Leith:
    Evaluating Impact of User-Cluster Targeted Attacks in Matrix Factorisation Recommenders. 15:1-15:34
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    Thanet Markchom, Huizhi Liang, James M. Ferryman:
    Explainable Meta-Path Based Recommender Systems. 16:1-16:28
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    Dairui Liu, Derek Greene, Irene Li, Xuefei Jiang, Ruihai Dong:
    Topic-Centric Explanations for News Recommendation. 17:1-17:25
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    Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo, Christina Lioma:
    Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study. 18:1-18:52
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    Thorsten Krause, Alina Deriyeva, Jan Heinrich Beinke, Gerrit York Bartels, Oliver Thomas:
    Mitigating Exposure Bias in Recommender Systems - A Comparative Analysis of Discrete Choice Models. 19:1-19:37
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    Zohreh Ovaisi, Parsa Saadatpanah, Shahin Sefati, Mesrob I. Ohannessian, Elena Zheleva:
    Fairness of Interaction in Ranking under Position, Selection, and Trust Bias. 20:1-20:28
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    Amanda Aird, Paresha Farastu, Joshua Sun, Elena Stefancova, Cassidy All, Amy Voida, Nicholas Mattei, Robin Burke:
    Dynamic Fairness-aware Recommendation Through Multi-agent Social Choice. 21:1-21:35

Regular Papers

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    Yang Zhang, Zhiyu Hu, Yimeng Bai, Jiancan Wu, Qifan Wang, Fuli Feng:
    Recommendation Unlearning via Influence Function. 22:1-22:23
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    Dietmar Jannach, Markus Zanker:
    A Survey on Intent-aware Recommender Systems. 23:1-23:32
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    Luciano Caroprese, Francesco Sergio Pisani, Bruno Miguel Veloso, Matthias König, Giuseppe Manco, Holger H. Hoos, João Gama:
    Modelling Concept Drift in Dynamic Data Streams for Recommender Systems. 24:1-24:28
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    Ziyang Liu, Chaokun Wang, Shuwen Zheng, Cheng Wu, Kai Zheng, Yang Song, Na Mou:
    Pone-GNN: Integrating Positive and Negative Feedback in Graph Neural Networks for Recommender Systems. 25:1-25:23

Volume 3, Number 3, September 2025

Special Issue on Knowledge Transferring for Recommender Systems

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    Zhiwei Liu, Hao Peng, Caiming Xiong, Julian J. McAuley, Philip S. Yu:
    Introduction to the Special Issue on Knowledge Transferring for Recommender Systems. 26:1-26:4
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    Zefeng Chen, Wensheng Gan, Jiayang Wu, Kaixia Hu, Hong Lin:
    Data Scarcity in Recommendation Systems: A Survey. 27:1-27:31
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    Yiming Hei, Jianwei Liu, Shicheng Wang, Lihong Wang, Zhengtao Yu, Yong Yang:
    GIIE: A Graph-based News Recommendation Model with Intrinsic Interest Enhancement. 28:1-28:23
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    Zhe Fu, Xi Niu, Xiangcheng Wu, Ruhani Rahman:
    A Deep Learning Model for Cross-Domain Serendipity Recommendations. 29:1-29:21
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    Bilal Khan, Jia Wu, Jian Yang, Xiaoxiao Ma:
    Heterogeneous Hypergraph Neural Network for Social Recommendation using Attention Network. 30:1-30:22
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    Xiaoyu Du, Kun Qian, Yunshan Ma, Xinguang Xiang:
    Enhancing Item-level Bundle Representation for Bundle Recommendation. 31:1-31:21
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    Shenghao Liu, Lingyun Lu, Bang Wang:
    Mining User-Item Interactions via Knowledge Graph for Recommendation. 32:1-32:19
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    Di Han, Xiaotian Jing, Yijun Chen, Junmin Liu, Kai Liao, Wenting Li:
    Cold-Start Recommendation based on Knowledge Graph and Meta-Learning under Positive and Negative sampling. 33:1-33:24
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    Yao Zhou, Haonan Wang, Jingrui He, Haixun Wang:
    Review-Based Explainable Recommendations: A Transparency Perspective. 34:1-34:20
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    Leigh Eytan, Veronika Bogina, Irad Ben-Gal, Noam Koenigstein:
    KPAR: Knowledge-aware Path-based Attentive Recommender with Interpretability. 35:1-35:23
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    Hongjian Shi, Yicheng Di, Xinyu Ruan, Mingrui Liao, Qian Zhang, Ruhui Ma, Haibing Guan:
    Efficient Federated Recommender System with Adaptive Model Pruning and Momentum-based Batch Adjustment. 36:1-36:23
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    Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Felice Antonio Merra, Tommaso Di Noia, Eugenio Di Sciascio:
    Formalizing Multimedia Recommendation through Multimodal Deep Learning. 37:1-37:33

WWW'23 Extended Papers

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    Seongku Kang, Wonbin Kweon, Dongha Lee, Jianxun Lian, Xing Xie, Hwanjo Yu:
    Unbiased, Effective, and Efficient Distillation from Heterogeneous Models for Recommender Systems. 38:1-38:28
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    Yuanhao Pu, Rui Fan, Jin Chen, Zhihao Zhu, Defu Lian, Enhong Chen:
    Automated Sparse and Low-Rank Shallow Autoencoders for Recommendation. 39:1-39:26
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    Yizhou Chen, Guangda Huzhang, Anxiang Zeng, Qingtao Yu, Hui Sun, Heng-Yi Li, Jingyi Li, Yabo Ni, Han Yu, Zhiming Zhou:
    Learning Personalizable Clustered Embedding for Recommender Systems. 40:1-40:38
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    Tunhou Zhang, Dehua Cheng, Yuchen He, Zhengxing Chen, Xiaoliang Dai, Liang Xiong, Yudong Liu, Feng Cheng, Yufan Cao, Feng Yan, Hai Li, Yiran Chen, Wei Wen:
    Towards Automated Model Design on Recommender Systems. 41:1-41:23

Volume 3, Number 4, December 2025

Highlights of RecSys '23

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    Yuhan Zhao, Rui Chen, Riwei Lai, Qilong Han, Hongtao Song, Li Chen:
    Denoising and Augmented Negative Sampling for Collaborative Filtering. 42:1-42:23
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    Zitao Xu, Shu Chen, Weike Pan, Zhong Ming:
    A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation. 43:1-43:28
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    Dugang Liu, Shenxian Xian, Yuhao Wu, Xiaolian Zhang, Zhong Ming:
    Pairwise Intent Graph Embedding Learning for Context-aware Recommendation with Knowledge Graph. 44:1-44:26
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    Kabir Nagrecha, Lingyi Liu, Pablo Delgado:
    Reinforcement Learning for Intra- & Inter-Node Recommender Data Pipeline Optimization. 45:1-45:29
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    Andreas Peintner, Amir Reza Mohammadi, Eva Zangerle:
    Efficient Session-based Recommendation with Contrastive Graph-based Shortest Path Search. 46:1-46:24
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    Aleksandr Vladimirovich Petrov, Craig Macdonald:
    Improving Effectiveness by Reducing Overconfidence in Large Catalogue Sequential Recommendation with gBCE loss. 47:1-47:34

Regular Papers

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    Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi:
    Impression-Aware Recommender Systems. 48:1-48:46
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    Kathrin Wardatzky, Oana Inel, Luca Rossetto, Abraham Bernstein:
    Whom do Explanations Serve? A Systematic Literature Survey of User Characteristics in Explainable Recommender Systems Evaluation. 49:1-49:35
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    Nimbeshaho Thierry, Ingabire Batamira Christ Chatelain, Zafar Ali, Pavlos Kefalas:
    Improved paper recommendation model incorporating knowledge graph embedding with IRGAN model. 50:1-50:28
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    Sajal Halder, Kwan Hui Lim, Jeffrey Chan, Xiuzhen Zhang:
    Deep Learning of Dynamic POI Generation and Optimisation for Itinerary Recommendation. 51:1-51:29
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    Zixuan Yi, Zijun Long, Iadh Ounis, Craig Macdonald, Richard McCreadie:
    Enhancing Recommender Systems: Deep Modality Alignment with Large Multi-Modal Encoders. 52:1-52:25
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    Keqin Bao, Jizhi Zhang, Wenjie Wang, Yang Zhang, Zhengyi Yang, Yanchen Luo, Chong Chen, Fuli Feng, Qi Tian:
    A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems. 53:1-53:27
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    Zhiying Deng, Jianjun Li, Wei Liu, Juan Zhao:
    Unbiased Interest Modeling in Sequential Basket Analysis: Addressing Repetition Bias with Multi-Factor Estimation. 54:1-54:27
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    Ayano Okoso, Keisuke Otaki, Satoshi Koide, Yukino Baba:
    Impact of Tone-Aware Explanations in Recommender Systems. 55:1-55:34
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    Shaowen Peng, Kazunari Sugiyama, Xin Liu, Tsunenori Mine:
    Balancing Embedding Spectrum for Recommendation. 56:1-56:25

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