第17届推荐系统年会（ACM RecSys）将在2023年9月18日到22日于新加坡举办。值得注意的是，本届年会是疫情发生以来的首次线下参会，大家可以在线下共享推荐系统领域的最新研究进展（面基）。并且这也是RecSys入选CCF推荐列表B类会议的首次会议，相信未来会有更多研究者瞄准这一会议进行投稿。
本年度的会议论文接收列表已于近日在官方网站全部公布，包括47篇长文论文和48篇短文论文。关于RecSys2023推荐系统长文整理可参考：RecSys2023推荐系统论文整理(长文)。本文侧重于整理已公布的推荐系统短文。
本年度的论文接收列表官网地址：
https://recsys.acm.org/recsys23/accepted-contributions/
通过对本次年会短文论文的总结发现，研究主题大概涵盖了基于大语言模型的推荐系统、视频推荐、协同过滤的改进、隐私保护推荐、序列推荐、冷启动推荐、公平性、长尾问题、社交推荐、对话推荐、多任务推荐、可解释推荐等。
最后，按照惯例为大家收集整理了该年会的论文列表，等论文正式发布后大家可以对自己感兴趣或者自己研究方向的论文进行更深入的阅读。
- A Probabilistic Position Bias Model for Short-Video Recommendation Feeds Olivier Jeunen
- Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation Ashraf Ghiye, Baptiste Barreau, Laurent Carlier and Michalis Vazirgiannis
- ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction Haoxuan Li, Taojun Hu, Zetong Xiong, Chunyuan Zheng, Fuli Feng, Xiangnan He and Xiao-Hua Zhou
- Adversarial Sleeping Bandit Problems with Multiple Plays: Algorithm and Ranking Application Jianjun Yuan, Wei Lee Woon and Ludovik Coba
- Analysis Operations for Constraint-based Recommender Systems Sebastian Lubos, Viet-Man Le, Alexander Felfernig and Thi Ngoc Trang Tran
- Beyond the Sequence: Statistics-driven Pre-training for Stabilizing Sequential Recommendation Model Sirui Wang, Peiguang Li, Yunsen Xian and Hongzhi Zhang
- Bootstrapped Personalized Popularity for Cold Start Recommender Systems Iason Chaimalas, Duncan Walker, Edoardo Gruppi, Ben Clark and Laura Toni
- Can ChatGPT Make Fair Recommendation? A Fairness Evaluation Benchmark for Recommendation with Large Language Model Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng and Xiangnan He
- Co-occurrence Embedding Enhancement for Long-tail Problem in Multi-Interest Recommendation Yaokun Liu, Xiaowang Zhang, Minghui Zou and Zhiyong Feng
- Collaborative filtering algorithms are prone to mainstream-taste bias Pantelis Analytis and Philipp Hager
- CR-SoRec: BERT driven Consistency Regularization for Social Recommendation Tushar Prakash, Raksha Jalan, Brijraj Singh and Naoyuki Onoe
- Deep Exploration for Recommendation Systems Zheqing Zhu and Benjamin Van Roy
- Deliberative Diversity for News Recommendations: Operationalization and Experimental User Study Lucien Heitz, Juliane A. Lischka, Rana Abdullah, Laura Laugwitz, Hendrik Meyer and Abraham Bernstein
- Enhancing Transformer without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation Vivian Lai, Huiyuan Chen, Chin-Chia Michael Yeh, Minghua Xu, Yiwei Cai and Hao Yang
- Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect Bruno Sguerra and Romain Hennequin
- Extended conversion: Capturing successful interactions in voice shopping Elad Haramaty, Zohar Karnin, Arnon Lazerson, Liane Lewin-Eytan and Yoelle Maarek
- Generative Next-Basket Recommendation Wenqi Sun, Ruobing Xie, Junjie Zhang, Wayne Xin Zhao, Leyu Lin and Ji-Rong Wen
- Group Fairness for Content Creators: the Role of Human and Algorithmic Biases under Popularity-based Recommendations Stefania Ionescu, Aniko Hannak and Nicolo Pagan
- Hessian-aware Quantized Node Embeddings for Recommendation Huiyuan Chen, Kaixiong Zhou, Kwei-Herng Lai, Chin-Chia Michael Yeh, Yan Zheng, Xia Hu and Hao Yang
- Incorporating Time in Sequential Recommendation Models Mostafa Rahmani, James Caverlee and Fei Wang
- Initiative transfer in conversational recommender systems Yuan Ma and Jürgen Ziegler
- Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation Marta Moscati, Christian Wallmann, Markus Reiter-Haas, Dominik Kowald, Elisabeth Lex and Markus Schedl
- Interface Design to Mitigate Inflation in Recommender Systems Rana Shahout, Yehonatan Peisakhovsky, Sasha Stoikov and Nikhil Garg
- Interpretable User Retention Modeling in Recommendation Rui Ding, Ruobing Xie, Xiaobo Hao, Xiaochun Yang, Kaikai Ge, Xu Zhang, Jie Zhou and Leyu Lin
- Large Language Model Augmented Narrative Driven Recommendations Sheshera Mysore, Andrew Mccallum and Hamed Zamani
- Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences Scott Sanner, Krisztian Balog, Filip Radlinski, Ben Wedin and Lucas Dixon
- LLM4Rec: Large Language Models for Recommendation via A Lightweight Tuning Framework Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng and Xiangnan He
- Looks Can Be Deceiving: Linking User-Item Interactions and User’s Propensity Towards Multi-Objective Recommendations Patrik Dokoupil, Ladislav Peska and Ludovico Boratto
- M3REC: A Meta-based Multi-scenario Multi-task Recommendation Framework Zerong Lan, Yingyi Zhang and Xianneng Li
- Multiple Connectivity Views for Session-based Recommendation Yaming Yang, Jieyu Zhang, Yujing Wang, Zheng Miao and Yunhai Tong
- Of Spiky SVDs and Music Recommendation Darius Afchar, Romain Hennequin and Vincent Guigue
- On the Consistency of Average Embeddings for Item Recommendation Walid Bendada, Guillaume Salha-Galvan, Romain Hennequin, Thomas Bouabça and Tristan Cazenave
- Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning Ruiyang Xu, Jalaj Bhandari, Dmytro Korenkevych, Fan Liu, Yuchen He, Alex Nikulkov and Zheqing Zhu
- Personalized Category Frequency prediction for Buy It Again recommendations Amit Pande, Kunal Ghosh and Rankyung Park
- Private Matrix Factorization with Public Item Features Mihaela Curmei, Walid Krichene and Li Zhang
- Progressive Horizon Learning: Adaptive Long Term Optimization for Personalized Recommendation Congrui Yi, David Zumwalt, Zijian Ni and Shreya Chakrabarti
- Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders Bjørnar Vassøy, Helge Langseth and Benjamin Kille
- ReCon: Reducing Congestion in Job Recommendation using Optimal Transport Yoosof Mashayekhi, Bo Kang, Jefrey Lijffijt and Tijl de Bie
- Scalable Approximate NonSymmetric Autoencoder for Collaborative Filtering Martin Spišák, Radek Bartyzal, Antonín Hoskovec, Ladislav Peška and Miroslav Tůma
- Scalable Deep Q-Learning for Session-Based Slate Recommendation Aayush Singha Roy, Edoardo D’Amico, Elias Tragos, Aonghus Lawlor and Neil Hurley
- Stability of Explainable Recommendation Sairamvinay Vijayaraghavan and Prasant Mohapatra
- Ti-DC-GNN: Incorporating Time-Interval Dual Graphs for Recommender Systems Nikita Severin, Andrey Savchenko, Dmitrii Kiselev, Maria Ivanova, Ivan Kireev and Ilya Makarov
- Time-Aware Item Weighting for the Next Basket Recommendations Aleksey Romanov, Oleg Lashinin, Marina Ananyeva and Sergey Kolesnikov
- Topic-Level Bayesian Surprise and Serendipity for Recommender Systems Tonmoy Hasan and Razvan Bunescu
- Towards Self-Explaining Sequence-Aware Recommendation Alejandro Ariza-Casabona, Maria Salamo and Ludovico Boratto
- Towards Sustainability-aware Recommender Systems: Analyzing the Trade-off Between Algorithms Performance and Carbon Footprint Giuseppe Spillo, Allegra De Filippo, Cataldo Musto, Michela Milano and Giovanni Semeraro
- Using Learnable Physics for Real-Time Exercise Form Recommendations Abhishek Jaiswal, Gautam Chauhan and Nisheeth Srivastava
- Widespread flaws in offline evaluation of recommender systems Balázs Hidasi and Ádám Tibor Czapp
