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Singapore
Conference
RecSys 2023
September 18 - 22, 2023
Singapore

Overview

The ACM Recommender Systems conference (RecSys) is the premier international forum for the presentation of new research results, systems, and techniques in the broad field of recommender systems. RecSys brings together the major international research groups working on recommender systems, along with many of the world’s leading companies active in e-commerce and other adjacent domains.

Sponsorship Details

Platinum
Conference website
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Amazon organizing committee members

Accepted publications

Workshops

RecSys 2023 Industry Talk
September 17
RecSys 2023 Workshop on Responsible Recommendation
September 18
Improving the accuracy of beauty product recommendations by assessing face illumination quality
Parnian Afshar, Jenny Yeon, Andriy Levitskyy, Rahul Suresh, Amin Banitalebi
2023
Computer vision
RecSys 2023 Workshop on Context-Aware Recommender Systems
September 18
Multi-task learning for reduced popularity bias in multi-territory video recommendations
Phanideep Gampa, Farnoosh Javadi, Belhassen Bayar, Ainur Yessenalina
2023
Machine learning
Design principles of robust multi-armed bandit framework in video recommendations
Belhassen Bayar, Phanideep Gampa, Ainur Yessenalina, Zhen Wen
2023
Machine learning
RecSys 2023 Workshop on MuRS: Music Recommender Systems
September 19
The workshop aims at bridging the existing gap between the diverse research communities focused on challenges specific to music recommender systems. The workshop will provide a space for researchers and practitioners from multiple disciplines, from both academia and industry, to exchange perspectives and to promote discussion on future research directions in the area of music recommender systems.

Amazon organizer: Tao Ye

Website: https://sites.google.com/view/murs
RecSys 2023 Workshop on Causality, Counterfactuals & Sequential Decision-Making (CONSEQUENCES)
September 19
Self-normalized off-policy estimators for ranking
Ben London, Alexander Buchholz, Giuseppe Di Benedetto, Jan Malte Lichtenberg, Yannik Stein, Thorsten Joachims
2023
Machine learning
Double clipping: Less-biased variance reduction in off-policy evaluation
Jan Malte Lichtenberg, Alexander Buchholz, Giuseppe Di Benedetto, Matteo Ruffini, Ben London
2023
Machine learning
Pre-trained recommender systems: A causal perspective
Ziqian Lin, Hao Ding, Nghia Hoang, Branislav Kveton, Anoop Deoras, Hao Wang
2023
Machine learning
Unbiased offline evaluation for learning to rank with business rules
Matej Jakimov, Alexander Buchholz, Yannik Stein, Thorsten Joachims
2023
Machine learning
RecSys 2023 Workshop on Recommender Systems in Fashion
September 18
Automated material properties extraction for enhanced beauty product discovery and makeup virtual try-on
Fatima Taheri Dezaki, Himanshu Arora, Rahul Suresh, Amin Banitalebi
2023
Machine learning
RecSys 2023 Workshop on Learning and Evaluating Recommendations with Impressions (LERI 2023)
September 19
The Workshop on Learning and Evaluating Recommendations with Impressions (LERI 2023) will focus on all aspects related to leveraging impression data to build and evaluate a recommendation engine. Impressions refer to the items displayed on the screen when a user interacts (or not) with the system and are the product of the whole recommendation engine, as such, impressions constitute a nuanced data source that raises novel research questions, opportunities, and challenges. These may have profound implications for how recommender systems are conceptualized, trained, and evaluated. The goal of LERI 2023 is to both help to coalesce researchers exploring the use of impressions from different perspectives, as well as foster increased interest from the community for this important and underexplored topic.

Amazon organizer: Pablo Castells

Website: https://recsyspolimi.github.io/leri2023/
Contextual position bias estimation using a single stochastic logging policy
Giuseppe Di Benedetto, Alexander Buchholz, Ben London, Matej Jakimov, Yannik Stein, Jan Malte Lichtenberg, Vito Bellini, Matteo Ruffini
2023
Machine learning
Impression-informed multi-behavior recommender system: A hierarchical graph attention approach
Dong Li, Divya Bhargavi, Vidya Sagar Ravipati
2023
Machine learning

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