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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 - Nikita Severin, Andrey V. Savchenko, Dmitrii Kiselev, Maria Ivanova, Ivan Kireev, Ilya Makarov:
Ti-DC-GNN: Incorporating Time-Interval Dual Graphs for Recommender Systems. 919-925 - Darius Afchar, Romain Hennequin, Vincent Guigue:
Of Spiky SVDs and Music Recommendation. 926-932 - Tonmoy Hasan, Razvan C. Bunescu:
Topic-Level Bayesian Surprise and Serendipity for Recommender Systems. 933-939 - Congrui Yi, David Zumwalt, Zijian Ni, Shreya Chakrabarti:
Progressive Horizon Learning: Adaptive Long Term Optimization for Personalized Recommendation. 940-946 - Sairamvinay Vijayaraghavan, Prasant Mohapatra:
Stability of Explainable Recommendation. 947-954 - Ruiyang Xu, Jalaj Bhandari, Dmytro Korenkevych, Fan Liu, Yuchen He, Alex Nikulkov, Zheqing Zhu:
Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning. 955-962 - Zheqing Zhu, Benjamin Van Roy:
Deep Exploration for Recommendation Systems. 963-970 - Bruno Sguerra, Viet-Anh Tran, Romain Hennequin:
Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect. 971-977 - Yuan Ma, Jürgen Ziegler:
Initiative transfer in conversational recommender systems. 978-984 - Aleksey Romanov, Oleg Lashinin, Marina Ananyeva, Sergey Kolesnikov:
Time-Aware Item Weighting for the Next Basket Recommendations. 985-992 - Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He:
Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation. 993-999 - Yaming Yang, Jieyu Zhang, Yujing Wang, Zheng Miao, Yunhai Tong:
Multiple Connectivity Views for Session-based Recommendation. 1000-1006 - Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He:
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. 1007-1014
Industry Posters
- Zongyi Wang, Yanyan Zou, Anyu Dai, Linfang Hou, Nan Qiao, Luobao Zou, Mian Ma, Zhuoye Ding, Sulong Xu:
An Industrial Framework for Personalized Serendipitous Recommendation in E-commerce. 1015-1018 - Manik Bhandari, Mingxian Wang, Oleg Poliannikov, Kanna Shimizu:
RecQR: Using Recommendation Systems for Query Reformulation to correct unseen errors in spoken dialog systems. 1019-1022 - Timo Wilm, Philipp Normann, Sophie Baumeister, Paul-Vincent Kobow:
Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions. 1023-1026 - Sarel Duanis, Keren Gaiger, Ravid Cohen, Shaked Zychlinski, Asnat Greenstein-Messica:
Visual Representation for Capturing Creator Theme in Brand-Creator Marketplace. 1027-1030 - M. Jeffrey Mei, Oliver Bembom, Andreas F. Ehmann:
Station and Track Attribute-Aware Music Personalization. 1031-1035 - Geetha Sai Aluri, Paul Greyson, Joaquin Delgado:
Optimizing Podcast Discovery: Unveiling Amazon Music's Retrieval and Ranking Framework. 1036-1038 - Konstantina Christakopoulou, Minmin Chen:
Towards Companion Recommenders Assisting Users' Long-Term Journeys. 1039-1041 - Yernat Assylbekov, Raghav Bali, Luke Bovard, Christian Klaue:
Delivery Hero Recommendation Dataset: A Novel Dataset for Benchmarking Recommendation Algorithms. 1042-1044 - Andreas Grün, Xenija Neufeld:
Transparently Serving the Public: Enhancing Public Service Media Values through Exploration. 1045-1048 - Yueqi Wang, Yoni Halpern, Shuo Chang, Jingchen Feng, Elaine Ya Le, Longfei Li, Xujian Liang, Min-Cheng Huang, Shane Li, Alex Beutel, Yaping Zhang, Shuchao Bi:
Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders. 1049-1053 - Yi Su, Minmin Chen:
Nonlinear Bandits Exploration for Recommendations. 1054-1057 - Ding Tong, Qifeng Qiao, Ting-Po Lee, James McInerney, Justin Basilico:
Navigating the Feedback Loop in Recommender Systems: Insights and Strategies from Industry Practice. 1058-1061 - Natalia Chen, Oinam Nganba Meetei, Nilothpal Talukder, Alexey Zankevich:
Leveling Up the Peloton Homescreen: A System and Algorithm for Dynamic Row Ranking. 1062-1066 - Johannes Kruse, Kasper Lindskow, Michael Riis Andersen, Jes Frellsen:
Creating the next generation of news experience on ekstrabladet.dk with recommender systems. 1067-1070 - Anshumali Shrivastava, Vihan Lakshman, Tharun Medini, Nicholas Meisburger, Joshua Engels, David Torres Ramos, Benito Geordie, Pratik Pranav, Shubh Gupta, Yashwanth Adunukota, Siddharth Jain:
From Research to Production: Towards Scalable and Sustainable Neural Recommendation Models on Commodity CPU Hardware. 1071-1074 - Jan Hartman, Assaf Klein, Davorin Kopic, Natalia Silberstein:
Unleash the Power of Context: Enhancing Large-Scale Recommender Systems with Context-Based Prediction Models. 1075-1077
Late-Breaking Results
- Blaz Skrlj, Blaz Mramor:
OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature Ranking. 1078-1083 - Anastasiia Klimashevskaia, Mehdi Elahi, Dietmar Jannach, Lars Skjærven, Astrid Tessem, Christoph Trattner:
Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study. 1084-1089 - Benedikt Loepp, Jürgen Ziegler:
How Users Ride the Carousel: Exploring the Design of Multi-List Recommender Interfaces From a User Perspective. 1090-1095 - Jesse Harte, Wouter Zorgdrager, Panos Louridas, Asterios Katsifodimos, Dietmar Jannach, Marios Fragkoulis:
Leveraging Large Language Models for Sequential Recommendation. 1096-1102 - Jiawei Zhang:
Learning the True Objectives of Multiple Tasks in Sequential Behavior Modeling. 1109-1113 - Andrea Bacciu, Federico Siciliano, Nicola Tonellotto, Fabrizio Silvestri:
Integrating Item Relevance in Training Loss for Sequential Recommender Systems. 1114-1119 - Anton Klenitskiy, Alexey Vasilev:
Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec? 1120-1125 - Sunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu, Zihua Si, Chen Xu, Zhongxiang Sun, Xiao Zhang, Jun Xu:
Uncovering ChatGPT's Capabilities in Recommender Systems. 1126-1132 - Jaime Hieu Do, Hady W. Lauw:
Continual Collaborative Filtering Through Gradient Alignment. 1133-1138 - Vincenzo Paparella, Dario Di Palma, Vito Walter Anelli, Tommaso Di Noia:
Broadening the Scope: Evaluating the Potential of Recommender Systems beyond prioritizing Accuracy. 1139-1145 - Ine Coppens, Luc Martens, Toon De Pessemier:
Analyzing Accuracy versus Diversity in a Health Recommender System for Physical Activities: a Longitudinal User Study. 1146-1151 - Yang Liu, Alan Medlar, Dorota Glowacka:
On the Consistency, Discriminative Power and Robustness of Sampled Metrics in Offline Top-N Recommender System Evaluation. 1152-1157 - Iustina Ivanova:
Climbing crags repetitive choices and recommendations. 1158-1164 - Patrícia Alves, André Martins, Paulo Novais, Goreti Marreiros:
Improving Group Recommendations using Personality, Dynamic Clustering and Multi-Agent MicroServices. 1165-1168 - Petr Kasalický, Antoine Ledent, Rodrigo Alves:
Uncertainty-adjusted Inductive Matrix Completion with Graph Neural Networks. 1169-1174 - Mesut Kaya, Toine Bogers:
An Exploration of Sentence-Pair Classification for Algorithmic Recruiting. 1175-1179 - Ergun Biçici:
Power Loss Function in Neural Networks for Predicting Click-Through Rate. 1180-1183 - Mehrdad Rostami, Mohammad Aliannejadi, Mourad Oussalah:
Towards Health-Aware Fairness in Food Recipe Recommendation. 1184-1189 - Amit Kumar Jaiswal, Yu Xiong:
A Model-Agnostic Framework for Recommendation via Interest-aware Item Embeddings. 1190-1195
Demonstrations
- Patrik Dokoupil, Ladislav Peska:
EasyStudy: Framework for Easy Deployment of User Studies on Recommender Systems. 1196-1199 - Douglas Turnbull, April Trainor, Douglas R. Turnbull, Elizabeth Richards, Kieran Bentley, Victoria Conrad, Paul Gagliano, Cassandra Raineault, Thorsten Joachims:
Localify.org: Locally-focus Music Artist and Event Recommendation. 1200-1203 - Arkadeep Acharya, Brijraj Singh, Naoyuki Onoe:
LLM Based Generation of Item-Description for Recommendation System. 1204-1207 - Antonela Tommasel, Rafael Pablos-Sarabia, Ira Assent:
Re2Dan: Retrieval of Medical Documents for e-Health in Danish. 1208-1211 - Tobias Vente, Michael D. Ekstrand, Joeran Beel:
Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit. 1212-1216
Workshops and Challenge
- Rahul Agrawal, Sarang Brahme, Sourav Maitra, Saikishore Kalloori, Abhishek Srivastava, Yong Liu, Athirai A. Irissappane:
RecSys Challenge 2023: Deep Funnel Optimization with a Focus on User Privacy. 1217-1220 - Alan Said, Eva Zangerle, Christine Bauer:
Third Workshop: Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2023). 1221-1222 - Olivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile, Yixin Wang:
CONSEQUENCES - The 2nd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. 1223-1226 - Andres Ferraro, Peter Knees, Massimo Quadrana, Tao Ye, Fabien Gouyon:
MuRS: Music Recommender Systems Workshop. 1227-1230 - Amon Rapp, Federica Cena, Christoph Trattner, Rita Orji, Julita Vassileva, Alain Starke:
BehavRec: Workshop on Recommendations for Behavior Change. 1231-1233 - Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Alexander Tuzhilin, Moshe Unger:
Workshop on Context-Aware Recommender Systems 2023. 1234-1236 - Julia Lasserre, Nima Dokoohaki, Reza Shirvany:
Fifth Workshop on Recommender Systems in Fashion and Retail - fashionXrecsys2023. 1237-1240 - Oana Inel, Nicolas Mattis, Milda Norkute, Alessandro Piscopo, Timothée Schmude, Sanne Vrijenhoek, Krisztian Balog:
QUARE: 2nd Workshop on Measuring the Quality of Explanations in Recommender Systems. 1241-1243 - Toine Bogers, David Graus, Mesut Kaya, Chris Johnson, Jens-Joris Decorte:
Third Workshop on Recommender Systems for Human Resources (RecSys in HR 2023). 1244-1247 - Maurizio Ferrari Dacrema, Pablo Castells, Justin Basilico, Paolo Cremonesi:
Workshop on Learning and Evaluating Recommendations with Impressions (LERI). 1248-1251 - Sanne Vrijenhoek, Lien Michiels, Johannes Kruse, Alain Starke, Nava Tintarev, Jordi Viader Guerrero:
NORMalize: The First Workshop on Normative Design and Evaluation of Recommender Systems. 1252-1254 - Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, Marco Polignano, Giovanni Semeraro, Martijn C. Willemsen:
10th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'23). 1255-1258 - Vito Walter Anelli, Pierpaolo Basile, Gerard de Melo, Francesco M. Donini, Antonio Ferrara, Cataldo Musto, Fedelucio Narducci, Azzurra Ragone, Markus Zanker:
Fifth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS). 1259-1262 - Benjamin Kille, Andreas Lommatzsch, Özlem Özgöbek, Peng Liu, Simen Eide, Lemei Zhang:
The Eleventh International Workshop on News Recommendation and Analytics (INRA'23). 1263-1266 - Michael D. Ekstrand, Jean Garcia-Gathright, Nasim Sonboli, Amifa Raj, Karlijn Dinnissen:
FAccTRec 2023: The 6th Workshop on Responsible Recommendation. 1267-1268 - Khushhall Chandra Mahajan, Amey Porobo Dharwadker, Saurabh Gupta, Brad Schumitsch:
VideoRecSys 2023: First Workshop on Large-Scale Video Recommender Systems. 1269-1271 - João Vinagre, Marie Al-Ghossein, Ladislav Peska, Alípio Mário Jorge, Albert Bifet:
ORSUM 2023 - 6th Workshop on Online Recommender Systems and User Modeling. 1272-1273 - Julia Neidhardt, Wolfgang Wörndl, Tsvi Kuflik, Dmitri Goldenberg, Markus Zanker:
Workshop on Recommenders in Tourism (RecTour) 2023. 1274-1275 - Ruiming Tang, Xiaoqiang Zhu, Junfeng Ge, Kuang-chih Lee, Biye Jiang, Xingxing Wang, Han Zhu, Tao Zhuang, Weiwen Liu, Kan Ren, Weinan Zhang, Xiangyu Zhao:
International Workshop on Deep Learning Practice for High-Dimensional Sparse Data with RecSys 2023. 1276-1280
Tutorials
- Wenyue Hua, Lei Li, Shuyuan Xu, Li Chen, Yongfeng Zhang:
Tutorial on Large Language Models for Recommendation. 1281-1283 - Aixin Sun:
On Challenges of Evaluating Recommender Systems in an Offline Setting. 1284-1285 - Weiwen Liu, Wei Guo, Yong Liu, Ruiming Tang, Hao Wang:
User Behavior Modeling with Deep Learning for Recommendation: Recent Advances. 1286-1287 - Markus Schedl, Vito Walter Anelli, Elisabeth Lex:
Trustworthy Recommender Systems: Technical, Ethical, Legal, and Regulatory Perspectives. 1288-1290 - Chuhan Wu, Qinglin Jia, Zhenhua Dong, Ruiming Tang:
Customer Lifetime Value Prediction: Towards the Paradigm Shift of Recommender System Objectives. 1293-1294
Doctoral Symposium
- Andreas Peintner:
Sequential Recommendation Models: A Graph-based Perspective. 1295-1299 - Jens Leysen:
Exploring Unlearning Methods to Ensure the Privacy, Security, and Usability of Recommender Systems. 1300-1304 - Rastislav Papso:
Complementary Product Recommendation for Long-tail Products. 1305-1311 - Giuseppe Spillo:
Knowledge-Aware Recommender Systems based on Multi-Modal Information Sources. 1312-1317 - Amir Reza Mohammadi:
Explainable Graph Neural Network Recommenders; Challenges and Opportunities. 1318-1324 - Tommaso Carraro:
Overcoming Recommendation Limitations with Neuro-Symbolic Integration. 1325-1331 - Lukas Wegmeth:
Improving Recommender Systems Through the Automation of Design Decisions. 1332-1338 - Alessio Ferrato:
Challenges for Anonymous Session-Based Recommender Systems in Indoor Environments. 1339-1341 - Imane Akdim:
Acknowledging Dynamic Aspects of Trust in Recommender Systems. 1342-1343 - Youchen Sun:
Denoising Explicit Social Signals for Robust Recommendation. 1344-1348 - Gangyi Zhang:
User-Centric Conversational Recommendation: Adapting the Need of User with Large Language Models. 1349-1354 - Tobias Vente:
Advancing Automation of Design Decisions in Recommender System Pipelines. 1355-1360 - Giacomo Balloccu:
Demystifying Recommender Systems: A Multi-faceted Examination of Explanation Generation, Impact, and Perception. 1361-1363 - Ziqing Wu:
Enhanced Privacy Preservation for Recommender Systems. 1364-1368 - Dario Di Palma:
Retrieval-augmented Recommender System: Enhancing Recommender Systems with Large Language Models. 1369-1373
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