What’s Trending in Recommendation Systems at KDD 2024? A Comprehensive Paper Overview

SIGKDD 2024 Research Track – Recommendation Papers

30th SIGKDD conference was held in Barcelona from August 25‑29, 2024. The Research Track received 2,046 valid submissions and accepted 20% of them (overall KDD2024 acceptance rate 22.10%). Among the accepted papers, 59 focus on recommendation systems.

Full list of Research Track papers: https://kdd2024.kdd.org/research-track-papers/

Large Model Recommendation Systems

RecExplainer: Aligning Large Language Models for Explaining Recommendation Models – Yuxuan Lei et al.

Bridging Items and Language: A Transition Paradigm for Large Language Model‑Based Recommendation – Xinyu Lin et al.

CoRAL: Collaborative Retrieval‑Augmented Large Language Models Improve Long‑tail Recommendation – Junda Wu et al.

Large Language Models meet Collaborative Filtering: An Efficient All‑round LLM‑based Recommender System – Sein Kim et al.

DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation – Kounianhua Du et al.

Adapting Job Recommendations to User Preference Drift with Behavioral‑Semantic Fusion Learning – Xiao Han et al.

EAGER: Two‑Stream Generative Recommender with Behavior‑Semantic Collaboration – Ye Wang et al.

CheatAgent: Attacking LLM‑Empowered Recommender Systems via LLM Agent – Liang‑bo Ning et al.

Graph‑Based Recommendation Algorithms

Towards Robust Recommendation via Decision Boundary‑aware Graph Contrastive Learning – Jiakai Tang et al.

GPFedRec: Graph‑Guided Personalization for Federated Recommendation – Chunxu Zhang et al.

How Powerful is Graph Filtering for Recommendation – Shaowen Peng et al.

Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering – Yihong Wu et al.

Graph Bottlenecked Social Recommendation – Yonghui Yang et al.

When Box Meets Graph Neural Network in Tag‑aware Recommendation – Fake Lin et al.

Consistency and Discrepancy‑Based Contrastive Tripartite Graph Learning for Recommendations – Linxin Guo et al.

Customizing Graph Neural Network for CAD Assembly Recommendation – Fengqi Liang, Huan Zhao et al.

Sequential Recommendation Algorithms

Explicit and Implicit Modeling via Dual‑Path Transformer for Behavior Set‑informed Sequential Recommendation – Ming Chen et al.

Probabilistic Attention for Sequential Recommendation – Yuli Liu et al.

Dataset Regeneration for Sequential Recommendation – Mingjia Yin et al.

Disentangled Multi‑interest Representation Learning for Sequential Recommendation – Yingpeng Du et al.

Pre‑Training with Transferable Attention for Addressing Market Shifts in Cross‑Market Sequential Recommendation – Chen Wang et al.

ROTAN: A Rotation‑based Temporal Attention Network for Time‑Specific Next POI Recommendation – Shanshan Feng et al.

Diffusion‑Based Cloud‑Edge‑Device Collaborative Learning for Next POI Recommendations – Jing Long et al.

Going Where, by Whom, and at What Time: Next Location Prediction Considering User Preference and Temporal Regularity – Tianao Sun et al.

DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation – Kairui Fu et al.

Fairness, Security, and Privacy in Recommendation

Where Have You Been? A Study of Privacy Risk for Point‑of‑Interest Recommendation – Kunlin Cai et al.

Performative Debias with Fair‑exposure Optimization Driven by Strategic Agents in Recommender Systems – Zhichen Xiang et al.

Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks – Zongwei Wang et al.

Debiased Recommendation with Noisy Feedback – Haoxuan Li et al.

A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News Recommendation – Shoujin Wang et al.

Harm Mitigation in Recommender Systems under User Preference Dynamics – Jerry Chee et al.

Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time – Haiyuan Zhao et al.

Computational Advertising

Robust Auto‑Bidding Strategies for Online Advertising – Qilong Lin et al.

InLN: Knowledge‑aware Incremental Leveling Network for Dynamic Advertising – Xujia Li et al.

Joint Auction in the Online Advertising Market – Zhen Zhang et al.

Auctions with LLM Summaries – Avinava Dubey et al.

Bi‑Objective Contract Allocation for Guaranteed Delivery Advertising – Yan Li et al.

Optimized Cost Per Click in Online Advertising: A Theoretical Analysis – Kaichen Zhang et al.

Truthful Bandit Mechanisms for Repeated Two‑stage Ad Auctions – Haoming Li et al.

An Efficient Local Search Algorithm for Large GD Advertising Inventory Allocation with Multilinear Constraints – Xiang He et al.

Debiasing and Denoising in Recommendation

Self‑Supervised Denoising through Independent Cascade Graph Augmentation for Robust Social Recommendation – Youchen Sun et al.

Double Correction Framework for Denoising Recommendation – Zhuangzhuang He et al.

Improving Multi‑modal Recommender Systems by Denoising and Aligning Multi‑modal Content and User Feedback – Guipeng Xv et al.

Popularity‑Aware Alignment and Contrast for Mitigating Popularity Bias – Miaomiao Cai et al.

Reinforcement Learning for Recommendation

Privileged Knowledge State Distillation for Reinforcement Learning‑based Educational Path Recommendation – Qingyao Li et al.

On (Normalised) Discounted Cumulative Gain as an Off‑Policy Evaluation Metric for Top‑Recommendation – Olivier Jeunen et al.

Maximum‑Entropy Regularized Decision Transformer with Reward Relabelling for Dynamic Recommendation – Xiaocong Chen et al.

Conversational Dueling Bandits in Generalized Linear Models – Shuhua Yang et al.

Other Topics

Natural Language Explainable Recommendation with Robustness Enhancement – Jingsen Zhang et al.

Rotative Factorization Machines – Zhen Tian et al.

Mitigating Negative Transfer in Cross‑Domain Recommendation via Knowledge Transferability Enhancement – Zijian Song et al.

Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method – Chen Yang et al.

Warming Up Cold‑Start CTR Prediction by Learning Item‑Specific Feature Interactions – Yaqing Wang et al.

Automatic Multi‑Task Learning Framework with Neural Architecture Search in Recommendations – Shen Jiang et al.

Continual Collaborative Distillation for Recommender System – Gyuseok Lee et al.

Relevance Meets Diversity: A User‑Centric Framework for Knowledge Exploration Through Recommendations – Erica Coppolillo et al.

Shopping Trajectory Representation Learning with Pre‑training for E‑commerce Customer Understanding and Recommendation – Yankai Chen et al.

Item‑Difficulty‑Aware Learning Path Recommendation: From a Real Walking Perspective – Haotian Zhang et al.

User Welfare Optimization in Recommender Systems with Competing Content Creators – Fan Yao et al.