Table of Contents

• Beyond Labels: Leveraging Deep Learning and LLMs for Content MetadataSaurabh Agrawal, John Trenkle, Jaya Kawale. 1 [doi]
• HUMMUS: A Linked, Healthiness-Aware, User-centered and Argument-Enabling Recipe Data Set for RecommendationFelix Bölz, Diana Nurbakova, Sylvie Calabretto, Armin Gerl, Lionel Brunie, Harald Kosch. 1-11 [doi]
• Integrating Offline Reinforcement Learning with Transformers for Sequential RecommendationXumei Xi, YuKe Zhao, Quan Liu, Liwen Ouyang, Yang Wu. 1 [doi]
• Recommenders In the wild - Practical Evaluation MethodsKim Falk, Morten Arngren. 1 [doi]
• Fast and Examination-agnostic Reciprocal Recommendation in Matching MarketsYoji Tomita, Riku Togashi, Yuriko Hashizume, Naoto Ohsaka. 12-23 [doi]
• ✨ Going Beyond Local: Global Graph-Enhanced Personalized News RecommendationsBoming Yang, Dairui Liu, Toyotaro Suzumura, Ruihai Dong, Irene Li. 24-34 [doi]
• Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery ShoppingMing Li, Mozhdeh Ariannezhad, Andrew Yates, Maarten de Rijke. 35-46 [doi]
• Full Index Deep Retrieval: End-to-End User and Item Structures for Cold-start and Long-tail Item RecommendationZhen Gong, Xin Wu, Lei Chen, Zhenzhe Zheng, Shengjie Wang, Anran Xu, Chong Wang, Fan Wu 0006. 47-57 [doi]
• SPARE: Shortest Path Global Item Relations for Efficient Session-based RecommendationAndreas Peintner, Amir Reza Mohammadi, Eva Zangerle. 58-69 [doi]
• Accelerating Creator Audience Building through Centralized ExplorationBuket Baran, Guilherme Dinis Junior, Antonina Danylenko, Olayinka S. Folorunso, Gösta Forsum, Maksym Lefarov, Lucas Maystre, Yu Zhao. 70-73 [doi]
• Distribution-based Learnable Filters with Side Information for Sequential RecommendationHaibo Liu, Zhixiang Deng, Liang Wang 0010, Jinjia Peng, Shi Feng 0001. 78-88 [doi]
• Reciprocal Sequential RecommendationBowen Zheng, Yupeng Hou, Wayne Xin Zhao, Yang Song, Hengshu Zhu. 89-100 [doi]
• STRec: Sparse Transformer for Sequential RecommendationsChengxi Li, Yejing Wang, Qidong Liu, Xiangyu Zhao, Wanyu Wang, Yiqi Wang, Lixin Zou, Wenqi Fan, Qing Li. 101-111 [doi]
• Track Mix Generation on Music Streaming Services using TransformersWalid Bendada, Théo Bontempelli, Mathieu Morlon, Benjamin Chapus, Thibault Cador, Thomas Bouabça, Guillaume Salha-Galvan. 112-115 [doi]
• gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative SamplingAleksandr Vladimirovich Petrov, Craig Macdonald. 116-128 [doi]
• Equivariant Contrastive Learning for Sequential RecommendationPeilin Zhou, Jingqi Gao, Yueqi Xie, Qichen Ye, Yining Hua, Jaeboum Kim, Shoujin Wang, Sunghun Kim 0001. 129-140 [doi]
• Contrastive Learning with Frequency-Domain Interest Trends for Sequential RecommendationYichi Zhang, Guisheng Yin, Yuxin Dong. 141-150 [doi]
• Task Aware Feature Extraction Framework for Sequential Dependence Multi-Task LearningXuewen Tao, Mingming Ha, Qiongxu Ma, Hongwei Cheng, Wenfang Lin, Xiaobo Guo, Linxun Cheng, Bing Han. 151-160 [doi]
• Gradient Matching for Categorical Data Distillation in CTR PredictionCheng Wang, Jiacheng Sun, Zhenhua Dong, Ruixuan Li 0001, Rui Zhang. 161-170 [doi]
• Deep Situation-Aware Interaction Network for Click-Through Rate PredictionYimin Lv, Shuli Wang, Beihong Jin, Yisong Yu, Yapeng Zhang, Jian Dong, Yongkang Wang 0011, Xingxing Wang, Dong Wang 0022. 171-182 [doi]
• AutoOpt: Automatic Hyperparameter Scheduling and Optimization for Deep Click-through Rate PredictionYujun Li, Xing Tang 0007, Bo Chen 0023, Yimin Huang, Ruiming Tang, Zhenguo Li. 183-194 [doi]
• Loss Harmonizing for Multi-Scenario CTR PredictionCongcong Liu, Liang Shi, Pei Wang, Fei Teng, Xue Jiang, Changping Peng, Zhangang Lin, JingPing Shao. 195-199 [doi]
• When Fairness meets Bias: a Debiased Framework for Fairness aware Top-N RecommendationJiakai Tang, Shiqi Shen, Zhipeng Wang, Zhi Gong, Jingsen Zhang, Xu Chen 0017. 200-210 [doi]
• Towards Robust Fairness-aware RecommendationHao Yang, Zhining Liu, Zeyu Zhang, Chenyi Zhuang, Xu Chen. 211-222 [doi]
• Two-sided Calibration for Quality-aware Responsible RecommendationChenyang Wang 0003, Yankai Liu, Yuanqing Yu, Weizhi Ma, Min Zhang, Yiqun Liu 0001, Haitao Zeng, Junlan Feng, Chao Deng. 223-233 [doi]
• RecAD: Towards A Unified Library for Recommender Attack and DefenseChangsheng Wang, Jianbai Ye, Wenjie Wang, Chongming Gao, Fuli Feng, Xiangnan He 0001. 234-244 [doi]
• Adversarial Collaborative Filtering for FreeHuiyuan Chen, Xiaoting Li 0001, Vivian Lai, Chin-Chia Michael Yeh, Yujie Fan, Yan Zheng 0001, Mahashweta Das, Hao Yang 0007. 245-255 [doi]
• Augmented Negative Sampling for Collaborative FilteringYuhan Zhao, Rui Chen, Riwei Lai, Qilong Han, Hongtao Song, Li Chen 0009. 256-266 [doi]
• Efficient Data Representation Learning in Google-scale SystemsDerek Zhiyuan Cheng, Ruoxi Wang, Wang-Cheng Kang, Benjamin Coleman, Yin Zhang 0011, Jianmo Ni, Jonathan Valverde, Lichan Hong, Ed H. Chi. 267-271 [doi]
• The Effect of Third Party Implementations on ReproducibilityBalázs Hidasi, Ádám Tibor Czapp. 272-282 [doi]
• Rethinking Multi-Interest Learning for Candidate Matching in Recommender SystemsYueqi Xie, Jingqi Gao, Peilin Zhou, Qichen Ye, Yining Hua, Jae Boum Kim, Fangzhao Wu, Sunghun Kim. 283-293 [doi]
• Trending Now: Modeling Trend RecommendationsHao Ding, Branislav Kveton, Yifei Ma, Youngsuk Park, Venkataramana Kini, Yupeng Gu, Ravi Divvela, Fei Wang, Anoop Deoras, Hao Wang. 294-305 [doi]
• A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta DistributionsNorman Knyazev, Harrie Oosterhuis. 306-317 [doi]
• Investigating the effects of incremental training on neural ranking modelsBenedikt Schifferer, Wenzhe Shi, Gabriel de Souza Pereira Moreira, Even Oldridge, Chris Deotte, Gilberto Titericz, Kazuki Onodera, Praveen Dhinwa, Vishal Agrawal, Chris Green. 318-321 [doi]
• Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item RecommendationYuwei Cao, Liangwei Yang, Chen Wang, Zhiwei Liu, Hao Peng, Chenyu You, Philip S. Yu. 322-333 [doi]
• LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement RecommendationDang-Minh Nguyen, Chenfei Wang, Yan Shen, Yifan Zeng. 334-337 [doi]
• Multi-Relational Contrastive Learning for RecommendationWei Wei, Lianghao Xia, Chao Huang. 338-349 [doi]
• Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven AnalysisVito Walter Anelli, Daniele Malitesta, Claudio Pomo, Alejandro Bellogín, Eugenio Di Sciascio, Tommaso Di Noia. 350-361 [doi]
• Goal-Oriented Multi-Modal Interactive Recommendation with Verbal and Non-Verbal Relevance FeedbackYaxiong Wu 0001, Craig Macdonald, Iadh Ounis. 362-373 [doi]
• Alleviating the Long-Tail Problem in Conversational Recommender SystemsZhipeng Zhao, Kun Zhou, Xiaolei Wang, Wayne Xin Zhao, Fan Pan, Zhao Cao, Ji-Rong Wen. 374-385 [doi]
• Data-free Knowledge Distillation for Reusing Recommendation ModelsCheng Wang, Jiacheng Sun, Zhenhua Dong, Jieming Zhu, Zhenguo Li, Ruixuan Li, Rui Zhang. 386-395 [doi]
• Reward innovation for long-term member satisfactionGary Tang, Jiangwei Pan, Henry Wang, Justin Basilico. 396-399 [doi]
• Contextual Multi-Armed Bandit for Email Layout RecommendationYan Chen, Emilian Vankov, Linas Baltrunas, Preston Donovan, Akash Mehta, Benjamin Schroeder, Matthew Herman. 400-402 [doi]
• Online Matching: A Real-time Bandit System for Large-scale RecommendationsXinyang Yi, Shao-Chuan Wang, Ruining He, Hariharan Chandrasekaran, Charles Wu, Lukasz Heldt, Lichan Hong, Minmin Chen, Ed H. Chi. 403-414 [doi]
• Incentivizing Exploration in Linear Contextual Bandits under Information GapHuazheng Wang, Haifeng Xu, Chuanhao Li, Zhiyuan Liu 0003, Hongning Wang. 415-425 [doi]
• AdaptEx: A Self-Service Contextual Bandit PlatformWilliam Black, Ercument Ilhan, Andrea Marchini, Vilda Markeviciute. 426-429 [doi]
• InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation ModelsKabir Nagrecha, Lingyi Liu, Pablo Delgado, Prasanna Padmanabhan. 430-442 [doi]
• Generative Learning Plan Recommendation for Employees: A Performance-aware Reinforcement Learning ApproachZhi Zheng, Ying Sun, Xin Song, Hengshu Zhu, Hui Xiong 0001. 443-454 [doi]
• Correcting for Interference in Experiments: A Case Study at DouyinVivek F. Farias, Hao Li, Tianyi Peng, Xinyuyang Ren, Huawei Zhang, Andrew Zheng. 455-466 [doi]
• Reproducibility of Multi-Objective Reinforcement Learning Recommendation: Interplay between Effectiveness and Beyond-Accuracy PerspectivesVincenzo Paparella, Vito Walter Anelli, Ludovico Boratto, Tommaso Di Noia. 467-478 [doi]
• DREAM: Decoupled Representation via Extraction Attention Module and Supervised Contrastive Learning for Cross-Domain Sequential RecommenderXiaoxin Ye, Yun Li, Lina Yao 0001. 479-490 [doi]
• A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential RecommendationZitao Xu, Weike Pan, Zhong Ming. 491-501 [doi]
• Exploring False Hard Negative Sample in Cross-Domain RecommendationHaokai Ma, Ruobing Xie, Lei Meng, Xin Chen, Xu Zhang, Leyu Lin, Jie Zhou. 502-514 [doi]
• Domain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain RecommendationJiajie Zhu, Yan Wang, Feng Zhu 0011, Zhu Sun. 515-527 [doi]
• Uncovering User Interest from Biased and Noised Watch Time in Video RecommendationHaiyuan Zhao, Lei Zhang, Jun Xu, Guohao Cai, Zhenhua Dong, Ji-Rong Wen. 528-539 [doi]
• Understanding and Modeling Passive-Negative Feedback for Short-video Sequential RecommendationYunzhu Pan, Chen Gao, Jianxin Chang, Yanan Niu, Yang Song, Kun Gai, Depeng Jin, Yong Li. 540-550 [doi]
• Personalised Recommendations for the BBC iPlayer: Initial approach and current challengesBenjamin Richard Clark, Kristine Grivcova, Polina Proutskova, Duncan Martin Walker. 551-553 [doi]
• Reproducibility Analysis of Recommender Systems relying on Visual Features: traps, pitfalls, and countermeasuresPasquale Lops, Elio Musacchio, Cataldo Musto, Marco Polignano, Antonio Silletti, Giovanni Semeraro. 554-564 [doi]
• Knowledge-based Multiple Adaptive Spaces Fusion for RecommendationMeng Yuan, Fuzhen Zhuang, Zhao Zhang, Deqing Wang, Jin Dong. 565-575 [doi]
• KGTORe: Tailored Recommendations through Knowledge-aware GNN ModelsAlberto Carlo Maria Mancino, Antonio Ferrara, Salvatore Bufi, Daniele Malitesta, Tommaso Di Noia, Eugenio Di Sciascio. 576-587 [doi]
• Pairwise Intent Graph Embedding Learning for Context-Aware RecommendationDugang Liu, Yuhao Wu, Weixin Li, Xiaolian Zhang, Hao Wang, Qinjuan Yang, Zhong Ming 0001. 588-598 [doi]
• Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLMBin Yin, Junjie Xie, Yu Qin, Zixiang Ding, Zhichao Feng, Xiang Li, Wei Lin. 599-601 [doi]
• STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based RepresentationWanda Li, Wenhao Zheng, Xuanji Xiao, Suhang Wang. 602-612 [doi]
• Disentangling Motives behind Item Consumption and Social Connection for Mutually-enhanced Joint PredictionYouchen Sun, Zhu Sun, Xiao Sha, Jie Zhang, Yew-Soon Ong. 613-624 [doi]
• BVAE: Behavior-aware Variational Autoencoder for Multi-Behavior Multi-Task RecommendationQianzhen Rao, Yang Liu, Weike Pan, Zhong Ming 0001. 625-636 [doi]
• MCM: A Multi-task Pre-trained Customer Model for PersonalizationRui Luo, Tianxin Wang, Jingyuan Deng, Peng Wan. 637-639 [doi]
• How Should We Measure Filter Bubbles? A Regression Model and Evidence for Online NewsLien Michiels, Jorre T. A. Vannieuwenhuyze, Jens Leysen, Robin Verachtert, Annelien Smets, Bart Goethals. 640-651 [doi]
• Everyone's a Winner! On Hyperparameter Tuning of Recommendation ModelsFaisal Shehzad, Dietmar Jannach. 652-657 [doi]
• What We Evaluate When We Evaluate Recommender Systems: Understanding Recommender Systems' Performance using Item Response TheoryYang Liu, Alan Medlar, Dorota Glowacka. 658-670 [doi]
• Identifying Controversial Pairs in Item-to-Item RecommendationsJunyi Shen, Dayvid V. R. Oliveira, Jin Cao, Brian Knott, Goodman Gu, Sindhu Vijaya Raghavan, Yunye Jin, Nikita Sudan, Rob Monarch. 671-674 [doi]
• A Probabilistic Position Bias Model for Short-Video Recommendation FeedsOlivier Jeunen. 675-681 [doi]
• ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction PredictionHaoxuan Li, Taojun Hu, Zetong Xiong, Chunyuan Zheng, Fuli Feng, Xiangnan He 0001, Xiao-Hua Zhou. 682-687 [doi]
• Using Learnable Physics for Real-Time Exercise Form RecommendationsAbhishek Jaiswal, Gautam Chauhan, Nisheeth Srivastava. 688-695 [doi]
• ReCon: Reducing Congestion in Job Recommendation using Optimal TransportYoosof Mashayekhi, Bo Kang, Jefrey Lijffijt, Tijl De Bie. 696-701 [doi]
• Interpretable User Retention Modeling in RecommendationRui Ding, Ruobing Xie, Xiaobo Hao, Xiaochun Yang, Kaikai Ge, Xu Zhang, Jie Zhou, Leyu Lin. 702-708 [doi]
• Analysis Operations for Constraint-based Recommender SystemsSebastian Lubos, Viet Man Le, Alexander Felfernig, Thi Ngoc Trang Tran. 709-714 [doi]
• Bootstrapped Personalized Popularity for Cold Start Recommender SystemsIason Chaimalas, Duncan Martin Walker, Edoardo Gruppi, Benjamin Richard Clark, Laura Toni. 715-722 [doi]
• Beyond the Sequence: Statistics-Driven Pre-training for Stabilizing Sequential Recommendation ModelSirui Wang, Peiguang Li, Yunsen Xian, Hongzhi Zhang. 723-729 [doi]
• Personalized Category Frequency prediction for Buy It Again recommendationsAmit Pande, Kunal Ghosh, Rankyung Park. 730-736 [doi]
• Generative Next-Basket RecommendationWenqi Sun, Ruobing Xie, Junjie Zhang, Wayne Xin Zhao, Leyu Lin, Ji-Rong Wen. 737-743 [doi]
• Adversarial Sleeping Bandit Problems with Multiple Plays: Algorithm and Ranking ApplicationJianjun Yuan, Wei Lee Woon, Ludovik Coba. 744-749 [doi]
• Collaborative filtering algorithms are prone to mainstream-taste biasPantelis Pipergias Analytis, Philipp Hager. 750-756 [doi]
• Hessian-aware Quantized Node Embeddings for RecommendationHuiyuan Chen, Kaixiong Zhou, Kwei-Herng Lai, Chin-Chia Michael Yeh, Yan Zheng, Xia Hu, Hao Yang 0007. 757-762 [doi]
• Scalable Approximate NonSymmetric Autoencoder for Collaborative FilteringMartin Spisák, Radek Bartyzal, Antonín Hoskovec, Ladislav Peska, Miroslav Tuma. 763-770 [doi]
• M3REC: A Meta-based Multi-scenario Multi-task Recommendation FrameworkZerong Lan, Yingyi Zhang, Xianneng Li. 771-776 [doi]
• Large Language Model Augmented Narrative Driven RecommendationsSheshera Mysore, Andrew McCallum, Hamed Zamani. 777-783 [doi]
• Incorporating Time in Sequential Recommendation ModelsMostafa Rahmani, James Caverlee, Fei Wang. 784-790 [doi]
• Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential RecommendationVivian Lai, Huiyuan Chen, Chin-Chia Michael Yeh, Minghua Xu 0003, Yiwei Cai, Hao Yang 0007. 791-797 [doi]
• Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product RecommendationAshraf Ghiye, Baptiste Barreau, Laurent Carlier, Michalis Vazirgiannis. 798-804 [doi]
• Private Matrix Factorization with Public Item FeaturesMihaela Curmei, Walid Krichene, Li Zhang 0001, Mukund Sundararajan. 805-812 [doi]
• Deliberative Diversity for News Recommendations: Operationalization and Experimental User StudyLucien Heitz, Juliane A. Lischka, Rana Abdullah, Laura Laugwitz, Hendrik Meyer, Abraham Bernstein. 813-819 [doi]
• Co-occurrence Embedding Enhancement for Long-tail Problem in Multi-Interest RecommendationYaokun Liu, Xiaowang Zhang, Minghui Zou, Zhiyong Feng 0002. 820-825 [doi]
• Extended Conversion: Capturing Successful Interactions in Voice ShoppingElad Haramaty, Zohar S. Karnin, Arnon Lazerson, Liane Lewin-Eytan, Yoelle Maarek. 826-832 [doi]
• On the Consistency of Average Embeddings for Item RecommendationWalid Bendada, Guillaume Salha-Galvan, Romain Hennequin, Thomas Bouabça, Tristan Cazenave. 833-839 [doi]
• Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music RecommendationMarta Moscati, Christian Wallmann, Markus Reiter-Haas, Dominik Kowald, Elisabeth Lex, Markus Schedl. 840-847 [doi]
• Widespread Flaws in Offline Evaluation of Recommender SystemsBalázs Hidasi, Ádám Tibor Czapp. 848-855 [doi]
• Towards Sustainability-aware Recommender Systems: Analyzing the Trade-off Between Algorithms Performance and Carbon FootprintGiuseppe Spillo, Allegra De Filippo, Cataldo Musto, Michela Milano, Giovanni Semeraro. 856-862 [doi]
• Group Fairness for Content Creators: the Role of Human and Algorithmic Biases under Popularity-based RecommendationsStefania Ionescu, Aniko Hannak, Nicolò Pagan. 863-870 [doi]
• Providing Previously Unseen Users Fair Recommendations Using Variational AutoencodersBjørnar Vassøy, Helge Langseth, Benjamin Kille. 871-876 [doi]
• Scalable Deep Q-Learning for Session-Based Slate RecommendationAayush Singha Roy, Edoardo D'Amico, Elias Z. Tragos, Aonghus Lawlor, Neil Hurley. 877-882 [doi]
• CR-SoRec: BERT driven Consistency Regularization for Social RecommendationTushar Prakash, Raksha Jalan, Brijraj Singh, Naoyuki Onoe. 883-889 [doi]
• Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based PreferencesScott Sanner, Krisztian Balog, Filip Radlinski, Ben Wedin, Lucas Dixon. 890-896 [doi]
• Interface Design to Mitigate Inflation in Recommender SystemsRana Shahout, Yehonatan Peisakhovsky, Sasha Stoikov, Nikhil Garg. 897-903 [doi]
• Towards Self-Explaining Sequence-Aware RecommendationAlejandro Ariza-Casabona, Maria Salamó, Ludovico Boratto, Gianni Fenu. 904-911 [doi]
• Looks Can Be Deceiving: Linking User-Item Interactions and User's Propensity Towards Multi-Objective RecommendationsPatrik Dokoupil, Ladislav Peska, Ludovico Boratto. 912-918 [doi]
• Ti-DC-GNN: Incorporating Time-Interval Dual Graphs for Recommender SystemsNikita Severin, Andrey V. Savchenko, Dmitrii Kiselev, Maria Ivanova, Ivan Kireev, Ilya Makarov. 919-925 [doi]
• Of Spiky SVDs and Music RecommendationDarius Afchar, Romain Hennequin, Vincent Guigue. 926-932 [doi]
• Topic-Level Bayesian Surprise and Serendipity for Recommender SystemsTonmoy Hasan, Razvan Bunescu. 933-939 [doi]
• Progressive Horizon Learning: Adaptive Long Term Optimization for Personalized RecommendationCongrui Yi, David Zumwalt, Zijian Ni, Shreya Chakrabarti. 940-946 [doi]
• Stability of Explainable RecommendationSairamvinay Vijayaraghavan, Prasant Mohapatra. 947-954 [doi]
• Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement LearningRuiyang Xu, Jalaj Bhandari, Dmytro Korenkevych, Fan Liu, Yuchen He, Alex Nikulkov, Zheqing Zhu. 955-962 [doi]
• Deep Exploration for Recommendation SystemsZheqing Zhu, Benjamin Van Roy. 963-970 [doi]
• Ex2Vec: Characterizing Users and Items from the Mere Exposure EffectBruno Sguerra, Viet-Anh Tran, Romain Hennequin. 971-977 [doi]
• Initiative transfer in conversational recommender systemsYuan Ma, Jürgen Ziegler 0001. 978-984 [doi]
• Time-Aware Item Weighting for the Next Basket RecommendationsAleksey Romanov, Oleg Lashinin, Marina Ananyeva, Sergey Kolesnikov. 985-992 [doi]
• Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model RecommendationJizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He 0001. 993-999 [doi]
• Multiple Connectivity Views for Session-based RecommendationYaming Yang 0001, Jieyu Zhang, Yujing Wang, Zheng Miao, Yunhai Tong. 1000-1006 [doi]
• TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with RecommendationKeqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He 0001. 1007-1014 [doi]
• An Industrial Framework for Personalized Serendipitous Recommendation in E-commerceZongyi Wang, Yanyan Zou, Anyu Dai, Linfang Hou, Nan Qiao, Luobao Zou, Mian Ma, Zhuoye Ding, Sulong Xu. 1015-1018 [doi]
• RecQR: Using Recommendation Systems for Query Reformulation to correct unseen errors in spoken dialog systemsManik Bhandari, Mingxian Wang, Oleg Poliannikov, Kanna Shimizu. 1019-1022 [doi]
• Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss FunctionsTimo Wilm, Philipp Normann, Sophie Baumeister, Paul-Vincent Kobow. 1023-1026 [doi]
• Visual Representation for Capturing Creator Theme in Brand-Creator MarketplaceSarel Duanis, Keren Gaiger, Ravid Cohen, Shaked Zychlinski, Asnat Greenstein-Messica. 1027-1030 [doi]
• Station and Track Attribute-Aware Music PersonalizationM. Jeffrey Mei, Oliver Bembom, Andreas F. Ehmann. 1031-1035 [doi]
• Optimizing Podcast Discovery: Unveiling Amazon Music's Retrieval and Ranking FrameworkGeetha Sai Aluri, Paul Greyson, Joaquin Delgado. 1036-1038 [doi]
• Towards Companion Recommenders Assisting Users' Long-Term JourneysKonstantina Christakopoulou, Minmin Chen. 1039-1041 [doi]
• Delivery Hero Recommendation Dataset: A Novel Dataset for Benchmarking Recommendation AlgorithmsYernat Assylbekov, Raghav Bali, Luke Bovard, Christian Klaue. 1042-1044 [doi]
• Transparently Serving the Public: Enhancing Public Service Media Values through ExplorationAndreas Grün, Xenija Neufeld. 1045-1048 [doi]
• Learning from Negative User Feedback and Measuring Responsiveness for Sequential RecommendersYueqi 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. 1049-1053 [doi]
• Nonlinear Bandits Exploration for RecommendationsYi Su, Minmin Chen. 1054-1057 [doi]
• Navigating the Feedback Loop in Recommender Systems: Insights and Strategies from Industry PracticeDing Tong, Qifeng Qiao, Ting-Po Lee, James McInerney, Justin Basilico. 1058-1061 [doi]
• Leveling Up the Peloton Homescreen: A System and Algorithm for Dynamic Row RankingNatalia Chen, Oinam Nganba Meetei, Nilothpal Talukder, Alexey Zankevich. 1062-1066 [doi]
• Creating the next generation of news experience on ekstrabladet.dk with recommender systemsJohannes Kruse, Kasper Lindskow, Michael Riis Andersen, Jes Frellsen. 1067-1070 [doi]
• From Research to Production: Towards Scalable and Sustainable Neural Recommendation Models on Commodity CPU HardwareAnshumali Shrivastava, Vihan Lakshman, Tharun Medini, Nicholas Meisburger, Joshua Engels, David Torres Ramos, Benito Geordie, Pratik Pranav, Shubh Gupta, Yashwanth Adunukota, Siddharth Jain. 1071-1074 [doi]
• Unleash the Power of Context: Enhancing Large-Scale Recommender Systems with Context-Based Prediction ModelsJan Hartman, Assaf Klein, Davorin Kopic, Natalia Silberstein. 1075-1077 [doi]
• OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature RankingBlaz Skrlj, Blaz Mramor. 1078-1083 [doi]
• Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field StudyAnastasiia Klimashevskaia, Mehdi Elahi, Dietmar Jannach, Lars Skjærven, Astrid Tessem, Christoph Trattner. 1084-1089 [doi]
• How Users Ride the Carousel: Exploring the Design of Multi-List Recommender Interfaces From a User PerspectiveBenedikt Loepp, Jürgen Ziegler 0001. 1090-1095 [doi]
• Leveraging Large Language Models for Sequential RecommendationJesse Harte, Wouter Zorgdrager, Panos Louridas, Asterios Katsifodimos, Dietmar Jannach, Marios Fragkoulis. 1096-1102 [doi]
• Learning the True Objectives of Multiple Tasks in Sequential Behavior ModelingJiawei Zhang. 1109-1113 [doi]
• Integrating Item Relevance in Training Loss for Sequential Recommender SystemsAndrea Bacciu, Federico Siciliano, Nicola Tonellotto, Fabrizio Silvestri. 1114-1119 [doi]
• Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?Anton Klenitskiy, Alexey Vasilev. 1120-1125 [doi]
• Uncovering ChatGPT's Capabilities in Recommender SystemsSunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu, Zihua Si, Chen Xu, Zhongxiang Sun, Xiao Zhang 0034, Jun Xu. 1126-1132 [doi]
• Continual Collaborative Filtering Through Gradient AlignmentJaime Hieu Do, Hady W. Lauw. 1133-1138 [doi]
• Broadening the Scope: Evaluating the Potential of Recommender Systems beyond prioritizing AccuracyVincenzo Paparella, Dario Di Palma, Vito Walter Anelli, Tommaso Di Noia. 1139-1145 [doi]
• Analyzing Accuracy versus Diversity in a Health Recommender System for Physical Activities: a Longitudinal User StudyIne Coppens, Luc Martens, Toon De Pessemier. 1146-1151 [doi]
• On the Consistency, Discriminative Power and Robustness of Sampled Metrics in Offline Top-N Recommender System EvaluationYang Liu, Alan Medlar, Dorota Glowacka. 1152-1157 [doi]
• Climbing crags repetitive choices and recommendationsIustina Ivanova. 1158-1164 [doi]
• Improving Group Recommendations using Personality, Dynamic Clustering and Multi-Agent MicroServicesPatrícia Alves, André Martins, Paulo Novais, Goreti Marreiros. 1165-1168 [doi]
• Uncertainty-adjusted Inductive Matrix Completion with Graph Neural NetworksPetr Kasalický, Antoine Ledent, Rodrigo Alves. 1169-1174 [doi]
• An Exploration of Sentence-Pair Classification for Algorithmic RecruitingMesut Kaya, Toine Bogers. 1175-1179 [doi]
• Power Loss Function in Neural Networks for Predicting Click-Through RateErgun Biçici. 1180-1183 [doi]
• Towards Health-Aware Fairness in Food Recipe RecommendationMehrdad Rostami, Mohammad Aliannejadi, Mourad Oussalah 0002. 1184-1189 [doi]
• A Model-Agnostic Framework for Recommendation via Interest-aware Item EmbeddingsAmit Kumar Jaiswal, Yu Xiong. 1190-1195 [doi]
• EasyStudy: Framework for Easy Deployment of User Studies on Recommender SystemsPatrik Dokoupil, Ladislav Peska. 1196-1199 [doi]
• Localify.org: Locally-focus Music Artist and Event RecommendationDouglas Turnbull, April Trainor, Douglas R. Turnbull, Elizabeth Richards, Kieran Bentley, Victoria Conrad, Paul Gagliano, Cassandra Raineault, Thorsten Joachims. 1200-1203 [doi]
• LLM Based Generation of Item-Description for Recommendation SystemArkadeep Acharya, Brijraj Singh, Naoyuki Onoe. 1204-1207 [doi]
• Re2Dan: Retrieval of Medical Documents for e-Health in DanishAntonela Tommasel, Rafael Pablos-Sarabia, Ira Assent. 1208-1211 [doi]
• Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) ToolkitTobias Vente, Michael Ekstrand, Joeran Beel. 1212-1216 [doi]
• RecSys Challenge 2023: Deep Funnel Optimization with a Focus on User PrivacyRahul Agrawal, Sarang Brahme, Sourav Maitra, Saikishore Kalloori, Abhishek Srivastava, Yong Liu, Athirai A. Irissappane. 1217-1220 [doi]
• Third Workshop: Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2023)Alan Said, Eva Zangerle, Christine Bauer 0001. 1221-1222 [doi]
• CONSEQUENCES - The 2nd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender SystemsOlivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile, Yixin Wang. 1223-1226 [doi]
• MuRS: Music Recommender Systems WorkshopAndres Ferraro, Peter Knees, Massimo Quadrana, Tao Ye, Fabien Gouyon. 1227-1230 [doi]
• BehavRec: Workshop on Recommendations for Behavior ChangeAmon Rapp, Federica Cena, Christoph Trattner, Rita Orji, Julita Vassileva, Alain Starke. 1231-1233 [doi]
• Workshop on Context-Aware Recommender Systems 2023Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Alexander Tuzhilin, Moshe Unger. 1234-1236 [doi]
• Fifth Workshop on Recommender Systems in Fashion and Retail - fashionXrecsys2023Julia Lasserre, Nima Dokoohaki, Reza Shirvany. 1237-1240 [doi]
• QUARE: 2nd Workshop on Measuring the Quality of Explanations in Recommender SystemsOana Inel, Nicolas Mattis, Milda Norkute, Alessandro Piscopo, Timothée Schmude, Sanne Vrijenhoek, Krisztian Balog. 1241-1243 [doi]
• Third Workshop on Recommender Systems for Human Resources (RecSys in HR 2023)Toine Bogers, David Graus, Mesut Kaya, Chris Johnson, Jens-Joris Decorte. 1244-1247 [doi]
• Workshop on Learning and Evaluating Recommendations with Impressions (LERI)Maurizio Ferrari Dacrema, Pablo Castells, Justin Basilico, Paolo Cremonesi. 1248-1251 [doi]
• NORMalize: The First Workshop on Normative Design and Evaluation of Recommender SystemsSanne Vrijenhoek, Lien Michiels, Johannes Kruse, Alain Starke, Nava Tintarev, Jordi Viader Guerrero. 1252-1254 [doi]
• 10th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'23)Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, Marco Polignano, Giovanni Semeraro, Martijn C. Willemsen. 1255-1258 [doi]
• Fifth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)Vito Walter Anelli, Pierpaolo Basile, Gerard de Melo, Francesco M. Donini, Antonio Ferrara, Cataldo Musto, Fedelucio Narducci, Azzurra Ragone, Markus Zanker. 1259-1262 [doi]
• The Eleventh International Workshop on News Recommendation and Analytics (INRA'23)Benjamin Kille, Andreas Lommatzsch, Özlem Özgöbek, Peng Liu, Simen Eide, Lemei Zhang. 1263-1266 [doi]
• FAccTRec 2023: The 6th Workshop on Responsible RecommendationMichael D. Ekstrand, Jean Garcia-Gathright, Nasim Sonboli, Amifa Raj, Karlijn Dinnissen. 1267-1268 [doi]
• VideoRecSys 2023: First Workshop on Large-Scale Video Recommender SystemsKhushhall Chandra Mahajan, Amey Porobo Dharwadker, Saurabh Gupta, Brad Schumitsch. 1269-1271 [doi]
• ORSUM 2023 - 6th Workshop on Online Recommender Systems and User ModelingJoão Vinagre, Marie Al-Ghossein, Ladislav Peska, Alípio Mário Jorge, Albert Bifet. 1272-1273 [doi]
• Workshop on Recommenders in Tourism (RecTour) 2023Julia Neidhardt, Wolfgang Wörndl, Tsvi Kuflik, Dmitri Goldenberg, Markus Zanker. 1274-1275 [doi]
• International Workshop on Deep Learning Practice for High-Dimensional Sparse Data with RecSys 2023Ruiming Tang, Xiaoqiang Zhu, Junfeng Ge, Kuang-Chih Lee, Biye Jiang, Xingxing Wang, Han Zhu, Tao Zhuang, Weiwen Liu, Kan Ren, Weinan Zhang 0001, Xiangyu Zhao. 1276-1280 [doi]
• Tutorial on Large Language Models for RecommendationWenyue Hua, Lei Li, Shuyuan Xu, Li Chen, Yongfeng Zhang. 1281-1283 [doi]
• On Challenges of Evaluating Recommender Systems in an Offline SettingAixin Sun. 1284-1285 [doi]
• User Behavior Modeling with Deep Learning for Recommendation: Recent AdvancesWeiwen Liu, Wei Guo 0006, Yong Liu, Ruiming Tang, Hao Wang. 1286-1287 [doi]
• Trustworthy Recommender Systems: Technical, Ethical, Legal, and Regulatory PerspectivesMarkus Schedl, Vito Walter Anelli, Elisabeth Lex. 1288-1290 [doi]
• Customer Lifetime Value Prediction: Towards the Paradigm Shift of Recommender System ObjectivesChuhan Wu, Qinglin Jia, Zhenhua Dong, Ruiming Tang. 1293-1294 [doi]
• Sequential Recommendation Models: A Graph-based PerspectiveAndreas Peintner. 1295-1299 [doi]
• Exploring Unlearning Methods to Ensure the Privacy, Security, and Usability of Recommender SystemsJens Leysen. 1300-1304 [doi]
• Complementary Product Recommendation for Long-tail ProductsRastislav Papso. 1305-1311 [doi]
• Knowledge-Aware Recommender Systems based on Multi-Modal Information SourcesGiuseppe Spillo. 1312-1317 [doi]
• Explainable Graph Neural Network Recommenders; Challenges and OpportunitiesAmir Reza Mohammadi. 1318-1324 [doi]
• Overcoming Recommendation Limitations with Neuro-Symbolic IntegrationTommaso Carraro. 1325-1331 [doi]
• Improving Recommender Systems Through the Automation of Design DecisionsLukas Wegmeth. 1332-1338 [doi]
• Challenges for Anonymous Session-Based Recommender Systems in Indoor EnvironmentsAlessio Ferrato. 1339-1341 [doi]
• Acknowledging Dynamic Aspects of Trust in Recommender SystemsImane Akdim. 1342-1343 [doi]
• Denoising Explicit Social Signals for Robust RecommendationYouchen Sun. 1344-1348 [doi]
• User-Centric Conversational Recommendation: Adapting the Need of User with Large Language ModelsGangyi Zhang. 1349-1354 [doi]
• Advancing Automation of Design Decisions in Recommender System PipelinesTobias Vente. 1355-1360 [doi]
• Demystifying Recommender Systems: A Multi-faceted Examination of Explanation Generation, Impact, and PerceptionGiacomo Balloccu. 1361-1363 [doi]
• Enhanced Privacy Preservation for Recommender SystemsZiqing Wu. 1364-1368 [doi]
• Retrieval-augmented Recommender System: Enhancing Recommender Systems with Large Language ModelsDario Di Palma. 1369-1373 [doi]