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今天这篇文章给大家汇总一下过去一年，头部大厂在顶会发布的推荐系统领域工作。在推荐系统领域，大厂的顶会工作相对有代表性，是最顶尖的推荐团队一段时间工作的精华，因此更有参考价值。
本文汇总了2024年KDD、WWW、WSDM等顶会中，阿里、腾讯、亚马逊等头部企业发表的推荐系统代表性工作，共49篇论文，涉及大模型、冷启动、消偏、多场景、多任务、多模态、兴趣建模、模型结构优化、CVR预估等11个推荐系统主要优化方向。
相关领域的详细工作解析和整理，后续也会逐渐在公众号中更新，感兴趣的同学欢迎持续关注。
RecSys——华为——大模型：FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction
RecSys——美团——大模型：LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding
RecSys——腾讯——大模型：Scaling Law of Large Sequential Recommendation Models
RecSys——腾讯——大模型：The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential Recommendation
RecSys——蚂蚁——大模型：ReLand: Integrating Large Language Models’ Insights into Industrial Recommenders via a Controllable Reasoning Pool
WWW——领英——大模型：Collaborative Large Language Model for Recommender Systems
WWW——华为——大模型：ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction
WWW——百度——大模型：Representation Learning with Large Language Models for Recommendation
KDD——微软——大模型：RecExplainer: Aligning Large Language Models for Explaining Recommendation Models
ICDE——腾讯——大模型：Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation
SIGIR——蚂蚁——大模型：Enhancing Sequential Recommenders with Augmented Knowledge from Aligned Large Language Models
NeurIPS——华为——大模型：LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation
AAAI——蚂蚁——大模型：Fine-Tuning Large Language Model Based Explainable Recommendation with Explainable Quality Reward
RecSys——快手——多模态：A Multi-modal Modeling Framework for Cold-start Short-video Recommendation
KDD——腾讯——多模态：Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback
RecSys——亚马逊——冷启动：MARec: Metadata Alignment for cold-start Recommendation
RecSys——快手——冷启动：Prompt Tuning for Item Cold-start Recommendation
KDD——百度——冷启动：Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions
ICDE——腾讯——冷启动：Firzen: Firing Strict Cold-Start Items with Frozen Heterogeneous and Homogeneous Graphs for Recommendation
RecSys——阿里——多场景：MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction
RecSys——美团——多场景：Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction
WWW——蚂蚁——多场景：Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
WWW——阿里——多场景：Not All Embeddings are Created Equal: Towards Robust Cross-domain Recommendation via Contrastive Learning
SIGIR——腾讯——多场景：Aiming at the Target: Filter Collaborative Information for Cross-Domain Recommendation
SIGIR——阿里——多场景：Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context
RecSys——腾讯——多任务：Touch the Core: Exploring Task Dependence Among Hybrid Targets for Recommendation
AAAI——腾讯——多任务：STEM: Unleashing the Power of Embeddings for Multi-task Recommendation
SIGIR——快手——多任务：MDMTRec: An Adaptive Multi-Task Multi-Domain Recommendation Framework
RecSys——快手——表示学习：MMGCL: Meta Knowledge-Enhanced Multi-view Graph Contrastive Learning for Recommendations
WWW——腾讯——表示学习：GraphPro: Graph Pre-training and Prompt Learning for Recommendation
KDD——亚马逊——表示学习：Shopping Trajectory Representation Learning with Pre-training for E-commerce Customer Understanding and Recommendation
RecSys——华为——CVR预估：Ranking-Aware Unbiased Post-Click Conversion Rate Estimation via AUC Optimization on Entire Exposure Space
RecSys——腾讯——CVR预估：Utilizing Non-click Samples via Semi-supervised Learning for Conversion Rate Prediction
WWW——腾讯——消偏：Causally Debiased Time-aware Recommendation
KDD——消偏——Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time
RecSys——华为——消偏：AIE: Auction Information Enhanced Framework for CTR Prediction in Online Advertising
RecSys——雅虎——特征优化：Low Rank Field-Weighted Factorization Machines for Low Latency Item Recommendation
AAAI——快手——时长预估：CREAD: A Classification-Restoration Framework with Error Adaptive Discretization for Watch Time Prediction in Video Recommender Systems
SIGIR——腾讯——CTR预估：Deep Pattern Network for Click-Through Rate Prediction
AAAI——京东——优化算法：Generalize for Future: Slow and Fast Trajectory Learning for CTR Prediction
WSDM——亚马逊——优化算法：To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders
WSDM——华为——兴趣建模：User Behavior Enriched Temporal Knowledge Graph for Sequential Recommendation
WSDM——快手——兴趣建模：Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential Recommendation
SIGIR——快手——疲劳度建模：Modeling User Fatigue for Sequential Recommendation
上述工作的详细解析，也会逐渐更新到公众号中，感兴趣的同学可以持续关注。
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