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ORCIDRecSys 2023: Singapore
- Jie Zhang, Li Chen, Shlomo Berkovsky, Min Zhang, Tommaso Di Noia, Justin Basilico, Luiz Pizzato, Yang Song:
Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, September 18-22, 2023. ACM 2023
Applications
- Felix Bölz
, Diana Nurbakova, Sylvie Calabretto, Armin Gerl, Lionel Brunie, Harald Kosch:
HUMMUS: A Linked, Healthiness-Aware, User-centered and Argument-Enabling Recipe Data Set for Recommendation. 1-11
Side Information, Items structure and Relations
- Saurabh Agrawal, John Trenkle, Jaya Kawale:
Beyond Labels: Leveraging Deep Learning and LLMs for Content Metadata. 1
Late-Breaking Results
- Xumei Xi, Yuke Zhao, Quan Liu, Liwen Ouyang, Yang Wu:
Integrating Offline Reinforcement Learning with Transformers for Sequential Recommendation. 1
Tutorials
- Kim Falk, Morten Arngren:
Recommenders In the wild - Practical Evaluation Methods. 1
Applications
- Yoji Tomita, Riku Togashi, Yuriko Hashizume, Naoto Ohsaka:
Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets. 12-23 - Boming Yang, Dairui Liu, Toyotaro Suzumura, Ruihai Dong, Irene Li:
✨ Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations. 24-34 - Ming Li, Mozhdeh Ariannezhad, Andrew Yates, Maarten de Rijke:
Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping. 35-46
Side Information, Items structure and Relations
- Zhen Gong, Xin Wu, Lei Chen, Zhenzhe Zheng, Shengjie Wang, Anran Xu, Chong Wang, Fan Wu:
Full Index Deep Retrieval: End-to-End User and Item Structures for Cold-start and Long-tail Item Recommendation. 47-57 - Andreas Peintner, Amir Reza Mohammadi, Eva Zangerle:
SPARE: Shortest Path Global Item Relations for Efficient Session-based Recommendation. 58-69 - Buket Baran, Guilherme Dinis Junior, Antonina Danylenko, Olayinka S. Folorunso, Gösta Forsum, Maksym Lefarov, Lucas Maystre, Yu Zhao:
Accelerating Creator Audience Building through Centralized Exploration. 70-73
Sequential Recommendation
- Haibo Liu, Zhixiang Deng, Liang Wang, Jinjia Peng, Shi Feng:
Distribution-based Learnable Filters with Side Information for Sequential Recommendation. 78-88 - Bowen Zheng, Yupeng Hou, Wayne Xin Zhao, Yang Song, Hengshu Zhu:
Reciprocal Sequential Recommendation. 89-100 - Chengxi Li, Yejing Wang, Qidong Liu, Xiangyu Zhao, Wanyu Wang, Yiqi Wang, Lixin Zou, Wenqi Fan, Qing Li:
STRec: Sparse Transformer for Sequential Recommendations. 101-111 - Walid Bendada, Théo Bontempelli, Mathieu Morlon, Benjamin Chapus, Thibault Cador, Thomas Bouabça, Guillaume Salha-Galvan:
Track Mix Generation on Music Streaming Services using Transformers. 112-115 - Aleksandr Vladimirovich Petrov, Craig MacDonald:
gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling. 116-128 - Peilin Zhou, Jingqi Gao, Yueqi Xie, Qichen Ye, Yining Hua, Jaeboum Kim, Shoujin Wang, Sunghun Kim:
Equivariant Contrastive Learning for Sequential Recommendation. 129-140 - Yichi Zhang, Guisheng Yin, Yuxin Dong:
Contrastive Learning with Frequency-Domain Interest Trends for Sequential Recommendation. 141-150 - Xuewen Tao, Mingming Ha, Qiongxu Ma, Hongwei Cheng, Wenfang Lin, Xiaobo Guo, Linxun Chen, Bing Han:
Task Aware Feature Extraction Framework for Sequential Dependence Multi-Task Learning. 151-160
Click-Through Rate Prediction
- Cheng Wang, Jiacheng Sun, Zhenhua Dong, Ruixuan Li, Rui Zhang:
Gradient Matching for Categorical Data Distillation in CTR Prediction. 161-170 - Yimin Lv, Shuli Wang, Beihong Jin, Yisong Yu, Yapeng Zhang, Jian Dong, Yongkang Wang, Xingxing Wang, Dong Wang:
Deep Situation-Aware Interaction Network for Click-Through Rate Prediction. 171-182 - Yujun Li, Xing Tang, Bo Chen, Yimin Huang, Ruiming Tang, Zhenguo Li:
AutoOpt: Automatic Hyperparameter Scheduling and Optimization for Deep Click-through Rate Prediction. 183-194 - Congcong Liu, Liang Shi, Pei Wang, Fei Teng, Xue Jiang, Changping Peng, Zhangang Lin, Jingping Shao:
Loss Harmonizing for Multi-Scenario CTR Prediction. 195-199
Trustworthy Recommendation
- Jiakai Tang, Shiqi Shen, Zhipeng Wang, Zhi Gong, Jingsen Zhang, Xu Chen:
When Fairness meets Bias: a Debiased Framework for Fairness aware Top-N Recommendation. 200-210 - Hao Yang, Zhining Liu, Zeyu Zhang, Chenyi Zhuang, Xu Chen:
Towards Robust Fairness-aware Recommendation. 211-222 - Chenyang Wang, Yankai Liu, Yuanqing Yu, Weizhi Ma, Min Zhang, Yiqun Liu, Haitao Zeng, Junlan Feng, Chao Deng:
Two-sided Calibration for Quality-aware Responsible Recommendation. 223-233 - Changsheng Wang, Jianbai Ye, Wenjie Wang, Chongming Gao, Fuli Feng, Xiangnan He:
RecAD: Towards A Unified Library for Recommender Attack and Defense. 234-244
Collaborative filtering
- Huiyuan Chen, Xiaoting Li, Vivian Lai, Chin-Chia Michael Yeh, Yujie Fan, Yan Zheng, Mahashweta Das, Hao Yang:
Adversarial Collaborative Filtering for Free. 245-255 - Yuhan Zhao, Rui Chen, Riwei Lai, Qilong Han, Hongtao Song, Li Chen:
Augmented Negative Sampling for Collaborative Filtering. 256-266 - Derek Zhiyuan Cheng, Ruoxi Wang, Wang-Cheng Kang, Benjamin Coleman, Yin Zhang, Jianmo Ni, Jonathan Valverde, Lichan Hong, Ed H. Chi:
Efficient Data Representation Learning in Google-scale Systems. 267-271 - Balázs Hidasi, Ádám Tibor Czapp:
The Effect of Third Party Implementations on Reproducibility. 272-282 - Yueqi Xie, Jingqi Gao, Peilin Zhou, Qichen Ye, Yining Hua, Jae Boum Kim, Fangzhao Wu, Sunghun Kim:
Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems. 283-293 - Hao Ding, Branislav Kveton, Yifei Ma, Youngsuk Park, Venkataramana Kini, Yupeng Gu, Ravi Divvela, Fei Wang, Anoop Deoras, Hao Wang:
Trending Now: Modeling Trend Recommendations. 294-305 - Norman Knyazev, Harrie Oosterhuis:
A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions. 306-317 - Benedikt Schifferer, Wenzhe Shi, Gabriel de Souza Pereira Moreira, Even Oldridge, Chris Deotte, Gilberto Titericz, Kazuki Onodera, Praveen Dhinwa, Vishal Agrawal, Chris Green:
Investigating the effects of incremental training on neural ranking models. 318-321
Graphs
- Yuwei Cao, Liangwei Yang, Chen Wang, Zhiwei Liu, Hao Peng, Chenyu You, Philip S. Yu:
Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation. 322-333 - Dang Minh Nguyen, Chenfei Wang, Yan Shen, Yifan Zeng:
LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation. 334-337 - Wei Wei, Lianghao Xia, Chao Huang:
Multi-Relational Contrastive Learning for Recommendation. 338-349 - Vito Walter Anelli, Daniele Malitesta, Claudio Pomo, Alejandro Bellogín, Eugenio Di Sciascio, Tommaso Di Noia:
Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis. 350-361
Interactive Recommendation
- Yaxiong Wu, Craig Macdonald, Iadh Ounis:
Goal-Oriented Multi-Modal Interactive Recommendation with Verbal and Non-Verbal Relevance Feedback. 362-373 - Zhipeng Zhao, Kun Zhou, Xiaolei Wang, Wayne Xin Zhao, Fan Pan, Zhao Cao, Ji-Rong Wen:
Alleviating the Long-Tail Problem in Conversational Recommender Systems. 374-385 - Cheng Wang, Jiacheng Sun, Zhenhua Dong, Jieming Zhu, Zhenguo Li, Ruixuan Li, Rui Zhang:
Data-free Knowledge Distillation for Reusing Recommendation Models. 386-395 - Gary Tang, Jiangwei Pan, Henry Wang, Justin Basilico:
Reward innovation for long-term member satisfaction. 396-399 - Yan Chen, Emilian Vankov, Linas Baltrunas, Preston Donovan, Akash Mehta, Benjamin Schroeder, Matthew Herman:
Contextual Multi-Armed Bandit for Email Layout Recommendation. 400-402 - Xinyang Yi, Shao-Chuan Wang, Ruining He, Hariharan Chandrasekaran, Charles Wu, Lukasz Heldt, Lichan Hong, Minmin Chen, Ed H. Chi:
Online Matching: A Real-time Bandit System for Large-scale Recommendations. 403-414 - Huazheng Wang, Haifeng Xu, Chuanhao Li, Zhiyuan Liu, Hongning Wang:
Incentivizing Exploration in Linear Contextual Bandits under Information Gap. 415-425 - William Black, Ercument Ilhan, Andrea Marchini, Vilda Markeviciute:
AdaptEx: A Self-Service Contextual Bandit Platform. 426-429
Reinforcement Learning
- Kabir Nagrecha, Lingyi Liu, Pablo Delgado, Prasanna Padmanabhan:
InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models. 430-442 - Zhi Zheng, Ying Sun, Xin Song, Hengshu Zhu, Hui Xiong:
Generative Learning Plan Recommendation for Employees: A Performance-aware Reinforcement Learning Approach. 443-454 - Vivek F. Farias, Hao Li, Tianyi Peng, Xinyuyang Ren, Huawei Zhang, Andrew Zheng:
Correcting for Interference in Experiments: A Case Study at Douyin. 455-466 - Vincenzo Paparella, Vito Walter Anelli, Ludovico Boratto, Tommaso Di Noia:
Reproducibility of Multi-Objective Reinforcement Learning Recommendation: Interplay between Effectiveness and Beyond-Accuracy Perspectives. 467-478
Cross-domain Recommendation
- Xiaoxin Ye, Yun Li, Lina Yao:
DREAM: Decoupled Representation via Extraction Attention Module and Supervised Contrastive Learning for Cross-Domain Sequential Recommender. 479-490 - Zitao Xu, Weike Pan, Zhong Ming:
A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation. 491-501 - Haokai Ma, Ruobing Xie, Lei Meng, Xin Chen, Xu Zhang, Leyu Lin, Jie Zhou:
Exploring False Hard Negative Sample in Cross-Domain Recommendation. 502-514 - Jiajie Zhu, Yan Wang, Feng Zhu, Zhu Sun:
Domain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain Recommendation. 515-527
Multimedia Recommendation
- Haiyuan Zhao, Lei Zhang, Jun Xu, Guohao Cai, Zhenhua Dong, Ji-Rong Wen:
Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation. 528-539 - Yunzhu Pan, Chen Gao, Jianxin Chang, Yanan Niu, Yang Song, Kun Gai, Depeng Jin, Yong Li:
Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation. 540-550 - Benjamin Richard Clark, Kristine Grivcova, Polina Proutskova, Duncan Martin Walker:
Personalised Recommendations for the BBC iPlayer: Initial approach and current challenges. 551-553 - Pasquale Lops, Elio Musacchio, Cataldo Musto, Marco Polignano, Antonio Silletti, Giovanni Semeraro:
Reproducibility Analysis of Recommender Systems relying on Visual Features: traps, pitfalls, and countermeasures. 554-564
Knowledge and Context
- Meng Yuan, Fuzhen Zhuang, Zhao Zhang, Deqing Wang, Jin Dong:
Knowledge-based Multiple Adaptive Spaces Fusion for Recommendation. 565-575 - Alberto Carlo Maria Mancino, Antonio Ferrara, Salvatore Bufi, Daniele Malitesta, Tommaso Di Noia, Eugenio Di Sciascio:
KGTORe: Tailored Recommendations through Knowledge-aware GNN Models. 576-587 - Dugang Liu, Yuhao Wu, Weixin Li, Xiaolian Zhang, Hao Wang, Qinjuan Yang, Zhong Ming:
Pairwise Intent Graph Embedding Learning for Context-Aware Recommendation. 588-598 - Bin Yin, Junjie Xie, Yu Qin, Zixiang Ding, Zhichao Feng, Xiang Li, Wei Lin:
Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM. 599-601
Multi-task Recommendation
- Wanda Li, Wenhao Zheng, Xuanji Xiao, Suhang Wang:
STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation. 602-612 - Youchen Sun, Zhu Sun, Xiao Sha, Jie Zhang, Yew Soon Ong:
Disentangling Motives behind Item Consumption and Social Connection for Mutually-enhanced Joint Prediction. 613-624 - Qianzhen Rao, Yang Liu, Weike Pan, Zhong Ming:
BVAE: Behavior-aware Variational Autoencoder for Multi-Behavior Multi-Task Recommendation. 625-636 - Rui Luo, Tianxin Wang, Jingyuan Deng, Peng Wan:
MCM: A Multi-task Pre-trained Customer Model for Personalization. 637-639
Evaluation
- Lien Michiels, Jorre T. A. Vannieuwenhuyze, Jens Leysen, Robin Verachtert, Annelien Smets, Bart Goethals:
How Should We Measure Filter Bubbles? A Regression Model and Evidence for Online News. 640-651 - Faisal Shehzad, Dietmar Jannach:
Everyone's a Winner! On Hyperparameter Tuning of Recommendation Models. 652-657 - Yang Liu, Alan Medlar, Dorota Glowacka:
What We Evaluate When We Evaluate Recommender Systems: Understanding Recommender Systems' Performance using Item Response Theory. 658-670 - Junyi Shen, Dayvid V. R. Oliveira, Jin Cao, Brian Knott, Goodman Gu, Sindhu Vijaya Raghavan, Yunye Jin, Nikita Sudan, Rob Monarch:
Identifying Controversial Pairs in Item-to-Item Recommendations. 671-674
Short Papers
- Olivier Jeunen:
A Probabilistic Position Bias Model for Short-Video Recommendation Feeds. 675-681 - Haoxuan Li, Taojun Hu, Zetong Xiong, Chunyuan Zheng, Fuli Feng, Xiangnan He, Xiao-Hua Zhou:
ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction. 682-687 - Abhishek Jaiswal, Gautam Chauhan, Nisheeth Srivastava:
Using Learnable Physics for Real-Time Exercise Form Recommendations. 688-695 - Yoosof Mashayekhi, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
ReCon: Reducing Congestion in Job Recommendation using Optimal Transport. 696-701 - Rui Ding, Ruobing Xie, Xiaobo Hao, Xiaochun Yang, Kaikai Ge, Xu Zhang, Jie Zhou, Leyu Lin:
Interpretable User Retention Modeling in Recommendation. 702-708 - Sebastian Lubos, Viet-Man Le, Alexander Felfernig, Thi Ngoc Trang Tran:
Analysis Operations for Constraint-based Recommender Systems. 709-714 - Iason Chaimalas, Duncan Martin Walker, Edoardo Gruppi, Benjamin Richard Clark, Laura Toni:
Bootstrapped Personalized Popularity for Cold Start Recommender Systems. 715-722 - Sirui Wang, Peiguang Li, Yunsen Xian, Hongzhi Zhang:
Beyond the Sequence: Statistics-Driven Pre-training for Stabilizing Sequential Recommendation Model. 723-729 - Amit Pande, Kunal Ghosh, Rankyung Park:
Personalized Category Frequency prediction for Buy It Again recommendations. 730-736 - Wenqi Sun, Ruobing Xie, Junjie Zhang, Wayne Xin Zhao, Leyu Lin, Ji-Rong Wen:
Generative Next-Basket Recommendation. 737-743 - Jianjun Yuan, Wei Lee Woon, Ludovik Coba:
Adversarial Sleeping Bandit Problems with Multiple Plays: Algorithm and Ranking Application. 744-749 - Pantelis Pipergias Analytis, Philipp Hager:
Collaborative filtering algorithms are prone to mainstream-taste bias. 750-756 - Huiyuan Chen, Kaixiong Zhou, Kwei-Herng Lai, Chin-Chia Michael Yeh, Yan Zheng, Xia Hu, Hao Yang:
Hessian-aware Quantized Node Embeddings for Recommendation. 757-762 - Martin Spisák, Radek Bartyzal, Antonín Hoskovec, Ladislav Peska, Miroslav Tuma:
Scalable Approximate NonSymmetric Autoencoder for Collaborative Filtering. 763-770 - Zerong Lan, Yingyi Zhang, Xianneng Li:
M3REC: A Meta-based Multi-scenario Multi-task Recommendation Framework. 771-776 - Sheshera Mysore, Andrew McCallum, Hamed Zamani:
Large Language Model Augmented Narrative Driven Recommendations. 777-783 - Mostafa Rahmani, James Caverlee, Fei Wang:
Incorporating Time in Sequential Recommendation Models. 784-790 - Vivian Lai, Huiyuan Chen, Chin-Chia Michael Yeh, Minghua Xu, Yiwei Cai, Hao Yang:
Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation. 791-797 - Ashraf Ghiye, Baptiste Barreau, Laurent Carlier, Michalis Vazirgiannis:
Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation. 798-804 - Mihaela Curmei, Walid Krichene, Li Zhang, Mukund Sundararajan:
Private Matrix Factorization with Public Item Features. 805-812 - Lucien Heitz, Juliane A. Lischka, Rana Abdullah, Laura Laugwitz, Hendrik Meyer, Abraham Bernstein:
Deliberative Diversity for News Recommendations: Operationalization and Experimental User Study. 813-819 - Yaokun Liu, Xiaowang Zhang, Minghui Zou, Zhiyong Feng:
Co-occurrence Embedding Enhancement for Long-tail Problem in Multi-Interest Recommendation. 820-825 - Elad Haramaty, Zohar S. Karnin, Arnon Lazerson, Liane Lewin-Eytan, Yoelle Maarek:
Extended Conversion: Capturing Successful Interactions in Voice Shopping. 826-832 - Walid Bendada, Guillaume Salha-Galvan, Romain Hennequin, Thomas Bouabça, Tristan Cazenave:
On the Consistency of Average Embeddings for Item Recommendation. 833-839 - Marta Moscati, Christian Wallmann, Markus Reiter-Haas, Dominik Kowald, Elisabeth Lex, Markus Schedl:
Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation. 840-847 - Balázs Hidasi, Ádám Tibor Czapp:
Widespread Flaws in Offline Evaluation of Recommender Systems. 848-855 - Giuseppe Spillo, Allegra De Filippo, Cataldo Musto, Michela Milano, Giovanni Semeraro:
Towards Sustainability-aware Recommender Systems: Analyzing the Trade-off Between Algorithms Performance and Carbon Footprint. 856-862 - Stefania Ionescu, Aniko Hannak, Nicolò Pagan:
Group Fairness for Content Creators: the Role of Human and Algorithmic Biases under Popularity-based Recommendations. 863-870 - Bjørnar Vassøy, Helge Langseth, Benjamin Kille:
Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders. 871-876 - Aayush Singha Roy, Edoardo D'Amico, Elias Z. Tragos, Aonghus Lawlor, Neil Hurley:
Scalable Deep Q-Learning for Session-Based Slate Recommendation. 877-882 - Tushar Prakash, Raksha Jalan, Brijraj Singh, Naoyuki Onoe:
CR-SoRec: BERT driven Consistency Regularization for Social Recommendation. 883-889 - Scott Sanner, Krisztian Balog, Filip Radlinski, Ben Wedin, Lucas Dixon:
Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences. 890-896 - Rana Shahout, Yehonatan Peisakhovsky, Sasha Stoikov, Nikhil Garg:
Interface Design to Mitigate Inflation in Recommender Systems. 897-903 - Alejandro Ariza-Casabona, Maria Salamó, Ludovico Boratto, Gianni Fenu:
Towards Self-Explaining Sequence-Aware Recommendation. 904-911 - Patrik Dokoupil, Ladislav Peska, Ludovico Boratto:
Looks Can Be Deceiving: Linking User-Item Interactions and User's Propensity Towards Multi-Objective Recommendations. 912-918