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RecSys 2024

A

ACM Conference on Recommender Systems

Official site ↗
228
accepted papers
213
authors
October 14-18, 2024
dates
Italy
location

228 / 228 papers

  1. 1
    "More to Read" at the Los Angeles Times: Solving a Cold Start Problem with LLMs to Improve Story Discovery
    Franklin Horn
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  2. 2
    11th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'24)
    Peter Brusilovsky
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  3. 3
    12th International Workshop on News Recommendation and Analytics (INRA'24)
    Benjamin Kille
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  4. 4
    A Comparative Analysis of Text-Based Explainable Recommender Systems
    Alejandro Ariza-Casabona
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  5. 5
    A Dataset for Adapting Recommender Systems to the Fashion Rental Economy
    Karl Audun Kagnes Borgersen
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  6. 6
    A Hybrid Multi-Agent Conversational Recommender System with LLM and Search Engine in E-commerce
    Guangtao Nie
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  7. 7
    A Multi-modal Modeling Framework for Cold-start Short-video Recommendation
    Gaode Chen
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  8. 8
    A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios
    Christian Ganhör
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  9. 9
    A New Perspective in Health Recommendations: Integration of Human Pose Estimation
    Gaetano Dibenedetto
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  10. 10
    A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph
    Daniele Malitesta
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  11. 11
    A Pre-trained Zero-shot Sequential Recommendation Framework via Popularity Dynamics
    Junting Wang
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  12. 12
    A Tool for Explainable Pension Fund Recommendations using Large Language Models
    Eduardo Alves da Silva
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  13. 13
    A Tutorial on Feature Interpretation in Recommender Systems
    Zhaocheng Du
    DBLP profile ↗ORCID 0000-0002-1811-129X ↗
  14. 14
    A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation
    Zixuan Yi
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  15. 15
    Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender Systems
    Yunfan Wu
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  16. 16
    Adaptive Fusion of Multi-View for Graph Contrastive Recommendation
    Mengduo Yang
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  17. 17
    AI-assisted Coding with Cody: Lessons from Context Retrieval and Evaluation for Code Recommendations
    Jan Hartman
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  18. 18
    AI-based Human-Centered Recommender Systems: Empirical Experiments and Research Infrastructure
    Ruixuan Sun
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  19. 19
    AIE: Auction Information Enhanced Framework for CTR Prediction in Online Advertising
    Yang Yang
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  20. 20
    AltRecSys: A Workshop on Alternative, Unexpected, and Critical Ideas in Recommendation
    Michael D. Ekstrand
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  21. 21
    AMBAR: A dataset for Assessing Multiple Beyond-Accuracy Recommenders
    Elizabeth Gómez
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  22. 22
    Analyzing User Preferences and Quality Improvement on Bing's WebPage Recommendation Experience with Large Language Models
    Jaidev Shah
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  23. 23
    Are We Explaining the Same Recommenders? Incorporating Recommender Performance for Evaluating Explainers
    Amir Reza Mohammadi
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  24. 24
    Balancing Habit Repetition and New Activity Exploration: A Longitudinal Micro-Randomized Trial in Physical Activity Recommendations
    Ine Coppens
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  25. 25
    Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation
    David Eric Austin
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  26. 26
    beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender Systems
    Vojtech Vancura
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  27. 27
    Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations
    Anima Singh
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  28. 28
    Bias in Book Recommendation
    Savvina Daniil
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  29. 29
    Biased User History Synthesis for Personalized Long-Tail Item Recommendation
    Keshav Balasubramanian
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  30. 30
    Bootstrapping Conditional Retrieval for User-to-Item Recommendations
    Hongtao Lin
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  31. 31
    Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other?
    Gustavo Penha
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  32. 32
    Bridging the Gap: Unpacking the Hidden Challenges in Knowledge Distillation for Online Ranking Systems
    Nikhil Khani
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  33. 33
    Bridging Viewpoints in News with Recommender Systems
    Jia Hua Jeng
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  34. 34
    Calibrating the Predictions for Top-N Recommendations
    Masahiro Sato
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  35. 35
    CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation
    Yaoyiran Li
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  36. 36
    Can Editorial Decisions Impair Journal Recommendations? Analysing the Impact of Journal Characteristics on Recommendation Systems
    Elias Entrup
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  37. 37
    CAPRI-FAIR: Integration of Multi-sided Fairness in Contextual POI Recommendation Framework
    Francis Zac dela Cruz
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  38. 38
    CEERS: Counterfactual Evaluations of Explanations in Recommender Systems
    Mikhail Baklanov
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  39. 39
    Co-optimize Content Generation and Consumption in a Large Scale Video Recommendation System
    Zhen Zhang
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  40. 40
    Comparative Analysis of Pretrained Audio Representations in Music Recommender Systems
    Yan-Martin Tamm
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  41. 41
    Computational Methods for Designing Human-Centered Recommender Systems: A Case Study Approach Intersecting Visual Arts and Healthcare
    Bereket Abera Yilma
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  42. 42
    Conducting Recommender Systems User Studies Using POPROX
    Robin Burke
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  43. 43
    Conducting User Experiments in Recommender Systems
    Bart P. Knijnenburg
    DBLP profile ↗ORCID 0000-0003-1341-0669 ↗
  44. 44
    ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning
    Xiao Yu
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  45. 45
    CONSEQUENCES - The 3rd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems
    Olivier Jeunen
    DBLP profile ↗ORCID 0000-0001-6256-5814 ↗
  46. 46
    Context-based Entity Recommendation for Knowledge Workers: Establishing a Benchmark on Real-life Data
    Mahta Bakhshizadeh
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  47. 47
    CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation
    Jieming Zhu
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  48. 48
    Country-diverted experiments for mitigation of network effects
    Lina Lin
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  49. 49
    Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization
    Abdulaziz Samra
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  50. 50
    Data Augmentation using Reverse Prompt for Cost-Efficient Cold-Start Recommendation
    Genki Kusano
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