04:25:00
FAccTRec 2023: The 6th Workshop on Responsible Recommendation
16:06
Journal Paper of the Year Awards: Leveraging affective hashtags for ranking music recommendations
15:29
Journal Paper of the Year Awards: Diversity by design in music recommender systems
18:58
Journal Paper of the Year Award: Effects and challenges of using a nutrition assistance system
14:51
Journal Paper of the Year Awards: A compositional model of multi faceted trust
17:24
Session 9: EANA: Reducing Privacy Risk on Large scale Recommendation Models
15:33
Session 9: Timely Personalization at Peloton:System and Algorithm for Boosting Time Relevant Content
13:52
Session 9: Evaluation Framework for Cold Start Technologies in Large Scale Production Settings
15:38
Session 9: A GPU specialized Inference Parameter Server for Large Scale Deep Recommendation Models
16:15
Session 9: An Incremental Learning framework for large scale CTR prediction
20:38
Session 9: Optimizing product recommendations for millions of merchants
16:56
Session 8: Revisiting the Performance of iALS on Item Recommendation Benchmarks
13:47
Session 8: You Say Factorization Machine, I Say Neural Network Itâs All in the Activation
15:06
Session 8: Dual Attentional Higher Order Factorization Machines
16:05
Session 8: Adversary or Friend? An adversarial Approach to Improving Recommender Systems
11:06
Session 8: MARRS: A Framework for multi-objective risk-aware route recommendation
17:56
Session 8: Fast And Accurate User Cold Start Learning Using Monte Carlo Tree Search
15:44
Session 7: Augmenting Netflix Search with In Session Adapted Recommendations
15:22
Session 7: Off Policy Actor Critic for Recommender Systems
15:39
Session 7:Self Supervised Bot Play for Transcript Free Conversational Recommendation with Rationales
17:35
Session 7: Streaming Session Based Recommendation: When Graph Neural Networks meet the Neighborhood
15:10
Session 7: A Lightweight Transformer for Next Item Product Recommendation
22:30
Session 7: Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Reco
16:33
Session 6: Recommendation as Language Processing RLP
15:35
Session 6: Bundle MCR: Towards Conversational Bundle Recommendation
16:08
Session 6: TorchRec: a PyTorch domain library for recommendation systems
15:03
Session 6: CAEN: A Hierarchically Attentive Evolution Network for Change Aware Recommendation
14:57
Session 6: Global and Personalized Graphs for Heterogeneous Sequential Recommendation
15:23
Session 6: TinyKG:Memory Efficient Training Framework for Knowledge Graph Neural Recommender Systems
15:27
Session 6: ProtoMF: Prototype based Matrix Factorization for Effective Explainable Recommendations
16:23
Session 6: BRUCE Bundle Recommendation Using Contextualized item Embeddings
14:40
Session 5: Exploiting Negative Preference in Content-based Music Reco with Contrastive Learning
14:43
Session 5: Reducing Cross-Topic Political Homogenization in Content-Based News Recommendation
39:54
Session 5: RADio Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendation
15:52
Session 5: Solving Diversity-Aware Maximum Inner Product Search Efficiently and Effectively
15:59
Session 5: Recommending for a Multi-Sided Marketplace with Heterogeneous Contents
18:32
Session 5: Donât recommend the obvious: estimate probability ratios
15:23
Session 4: Challenges in Translating Research to Practice for Evaluating Fairness and Bias
14:59
Session 4: Dynamic Global Sensitivity for Differentially Private Contextual Bandits
14:57
Session 4: Fairness-aware Federated Matrix Factorization
16:29
Session 4: Toward Fair Federated Recommendation Learning
30:57
Session 4: Countering Popularity Bias by Regularizing Score Differences
15:21
Session 4: Imbalanced Data Sparsity as Source of Unfair Bias in Collaborative Filtering
01:05:59
Keynote with Catherine D'Ignazio: CO-DESIGNING ML MODELS WITH DATA ACTIVISTS
17:53
Session 3: Translating the Public Service Media Remit into Metrics and Algorithms
16:14
Session 3: Rethinking Personalized Ranking at Pinterest: An End-to-End Approach
14:09
Session 2: [BEST PAPER AWARD] Denoising Self-Attentive Sequential Recommendation
16:45
Session 1: Towards Psychologically Grounded Dynamic Preference Models
24:58
Virtual AMA with Catherine D'Ignazio
16:51
Session 2: Effective and Efficient Training for Sequential Recommendation using Recency Sampling
01:07:37
Keynote Mor Naaman: âMY AI MUST HAVE BEEN BROKENâ: HOW AI STANDS TO RESHAPE HUMAN COMMUNICATION
14:59
Session 3: Identifying New Podcasts with High Appeal Using Pure Exploration Infinitely-Armed Bandit
11:10
Session 3: Modeling Two-Way Selection Preference for Person-Job Fit
12:32
Session 3: Learning Recommendations from User Actions in the Item-poor Insurance Domain
16:36
Session 3: Reusable Self-Attention Recommender Systems in Fashion Industry Applications
13:12
Session 3: Multi-Modal Dialog State Tracking for Interactive Fashion Recommendation
14:09
Session 1: Personalizing Benefits Allocation Without Spending Money
14:09
Session 1: Learning Usersâ Preferred Visual Styles in an Image Marketplace
26:39
Opening Ceremony
11:45
Session 1: Modeling User Repeat Consumption Behavior for Online Novel Recommendation
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About
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. Recommendation is a particular form of information filtering, that exploits past behaviors and user similarities to generate a list of information items that is personally tailored to an end-userâs preferences. As RecSys brings together the main international research groups working on recommender systems, along with many of the worldâs leading e-commerce companies, it has become the most important annual conference for the presentation and discussion of recommender systems research.
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