RecSys

Publications

2023

Ashraf Ghiye, Baptiste Barreau, Laurent Carlier, Michalis Vazirgiannis
Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation. RecSys (2023)
Alberto Carlo Maria Mancino, Antonio Ferrara, Salvatore Bufi, Daniele Malitesta, Tommaso Di Noia, Eugenio Di Sciascio
KGTORe: Tailored Recommendations through Knowledge-aware GNN Models. RecSys (2023)
Amir Reza Mohammadi
Explainable Graph Neural Network Recommenders; Challenges and Opportunities. RecSys (2023)
Andreas Grün, Xenija Neufeld
Transparently Serving the Public: Enhancing Public Service Media Values through Exploration. RecSys (2023)
Saurabh Agrawal, John Trenkle, Jaya Kawale
Beyond Labels: Leveraging Deep Learning and LLMs for Content Metadata. RecSys (2023)
Timo Wilm, Philipp Normann, Sophie Baumeister, Paul-Vincent Kobow
Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions. RecSys (2023)
M. Jeffrey Mei, Oliver Bembom, Andreas F. Ehmann
Station and Track Attribute-Aware Music Personalization. RecSys (2023)
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. RecSys (2023)
Sebastian Lubos, Viet-Man Le, Alexander Felfernig, Thi Ngoc Trang Tran
Analysis Operations for Constraint-based Recommender Systems. RecSys (2023)
Abhishek Jaiswal, Gautam Chauhan, Nisheeth Srivastava
Using Learnable Physics for Real-Time Exercise Form Recommendations. RecSys (2023)
Julia Lasserre, Nima Dokoohaki, Reza Shirvany
Fifth Workshop on Recommender Systems in Fashion and Retail - fashionXrecsys2023. RecSys (2023)
Alejandro Ariza-Casabona, Maria Salamó, Ludovico Boratto, Gianni Fenu
Towards Self-Explaining Sequence-Aware Recommendation. RecSys (2023)
Ine Coppens, Luc Martens, Toon De Pessemier
Analyzing Accuracy versus Diversity in a Health Recommender System for Physical Activities: a Longitudinal User Study. RecSys (2023)
Meng Yuan, Fuzhen Zhuang, Zhao Zhang, Deqing Wang, Jin Dong
Knowledge-based Multiple Adaptive Spaces Fusion for Recommendation. RecSys (2023)
Xumei Xi, Yuke Zhao, Quan Liu, Liwen Ouyang, Yang Wu
Integrating Offline Reinforcement Learning with Transformers for Sequential Recommendation. RecSys (2023)
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. RecSys (2023)
Wenyue Hua, Lei Li, Shuyuan Xu, Li Chen, Yongfeng Zhang
Tutorial on Large Language Models for Recommendation. RecSys (2023)
Rana Shahout, Yehonatan Peisakhovsky, Sasha Stoikov, Nikhil Garg
Interface Design to Mitigate Inflation in Recommender Systems. RecSys (2023)
Youchen Sun
Denoising Explicit Social Signals for Robust Recommendation. RecSys (2023)
Yuan Ma, Jürgen Ziegler
Initiative transfer in conversational recommender systems. RecSys (2023)
Norman Knyazev, Harrie Oosterhuis
A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions. RecSys (2023)
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. RecSys (2023)
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. RecSys (2023)
Walid Bendada, Guillaume Salha-Galvan, Romain Hennequin, Thomas Bouabça, Tristan Cazenave
On the Consistency of Average Embeddings for Item Recommendation. RecSys (2023)
Alessio Ferrato
Challenges for Anonymous Session-Based Recommender Systems in Indoor Environments. RecSys (2023)
Tobias Vente, Michael Ekstrand, Joeran Beel
Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit. RecSys (2023)
Amit Kumar Jaiswal, Yu Xiong
A Model-Agnostic Framework for Recommendation via Interest-aware Item Embeddings. RecSys (2023)
Jiawei Zhang
Learning the True Objectives of Multiple Tasks in Sequential Behavior Modeling. RecSys (2023)
Patrik Dokoupil, Ladislav Peska
EasyStudy: Framework for Easy Deployment of User Studies on Recommender Systems. RecSys (2023)
Yaokun Liu, Xiaowang Zhang, Minghui Zou, Zhiyong Feng
Co-occurrence Embedding Enhancement for Long-tail Problem in Multi-Interest Recommendation. RecSys (2023)
Weiwen Liu, Wei Guo, Yong Liu, Ruiming Tang, Hao Wang
User Behavior Modeling with Deep Learning for Recommendation: Recent Advances. RecSys (2023)
Gangyi Zhang
User-Centric Conversational Recommendation: Adapting the Need of User with Large Language Models. RecSys (2023)
Rastislav Papso
Complementary Product Recommendation for Long-tail Products. RecSys (2023)
Martin Spisák, Radek Bartyzal, Antonín Hoskovec, Ladislav Peska, Miroslav Tuma
Scalable Approximate NonSymmetric Autoencoder for Collaborative Filtering. RecSys (2023)
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. RecSys (2023)
Stefania Ionescu, Aniko Hannak, Nicolò Pagan
Group Fairness for Content Creators: the Role of Human and Algorithmic Biases under Popularity-based Recommendations. RecSys (2023)
Darius Afchar, Romain Hennequin, Vincent Guigue
Of Spiky SVDs and Music Recommendation. RecSys (2023)
Yang Liu, Alan Medlar, Dorota Glowacka
On the Consistency, Discriminative Power and Robustness of Sampled Metrics in Offline Top-N Recommender System Evaluation. RecSys (2023)
Andrea Bacciu, Federico Siciliano, Nicola Tonellotto, Fabrizio Silvestri
Integrating Item Relevance in Training Loss for Sequential Recommender Systems. RecSys (2023)
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. RecSys (2023)
Geetha Sai Aluri, Paul Greyson, Joaquin Delgado
Optimizing Podcast Discovery: Unveiling Amazon Music's Retrieval and Ranking Framework. RecSys (2023)
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. RecSys (2023)
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. RecSys (2023)
Yaming Yang, Jieyu Zhang, Yujing Wang, Zheng Miao, Yunhai Tong
Multiple Connectivity Views for Session-based Recommendation. RecSys (2023)
Natalia Chen, Oinam Nganba Meetei, Nilothpal Talukder, Alexey Zankevich
Leveling Up the Peloton Homescreen: A System and Algorithm for Dynamic Row Ranking. RecSys (2023)
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. RecSys (2023)
Tommaso Carraro
Overcoming Recommendation Limitations with Neuro-Symbolic Integration. RecSys (2023)
Yichi Zhang, Guisheng Yin, Yuxin Dong
Contrastive Learning with Frequency-Domain Interest Trends for Sequential Recommendation. RecSys (2023)
Changsheng Wang, Jianbai Ye, Wenjie Wang, Chongming Gao, Fuli Feng, Xiangnan He
RecAD: Towards A Unified Library for Recommender Attack and Defense. RecSys (2023)
Iason Chaimalas, Duncan Martin Walker, Edoardo Gruppi, Benjamin Richard Clark, Laura Toni
Bootstrapped Personalized Popularity for Cold Start Recommender Systems. RecSys (2023)