The ACM SIGKDD (International Conference on Data Mining and Knowledge Discovery, referred to as KDD) conference began in 1989. It is the oldest and largest international top academic conference in the field of data mining. The conference on concepts such as packages attracts a large number of research scholars and practitioners in the fields of data mining, machine learning, big data and artificial intelligence every year.
Through AI technology, AMiner sorted out the conference papers included in KDD2023, and today I will share the Recommendation theme paper! (Due to space constraints, this article only shows some papers. Click to read the original text to go directly to the KDD summit page to view all papers)
1.Adaptive Graph Contrastive Learning for Recommendation
Link: https://www.aminer.cn/pub/6466fafbd68f896efaeb7633/
2.Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation
Link: https://www.aminer.cn/pub/648000a9d68f896efaa123eb/
3.Multi-channel Integrated Recommendation with Exposure Constraints
Link: https://www.aminer.cn/pub/646c3ad0d68f896efa5ce60f/
4.ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop
Link: https://www.aminer.cn/pub/648bde68d68f896efaf81bd3/
5.Hierarchical Invariant Learning for Domain Generalization Recommendation
Link: https://www.aminer.cn/pub/64af9a073fda6d7f065a6d92/
6.Debiasing Recommendation by Learning Identifiable Latent Confounders
Link: https://www.aminer.cn/pub/63e9aa5e90e50fcafd133661/
7.Meta Graph Learning for Long-tail Recommendation
Link: https://www.aminer.cn/pub/64af9a033fda6d7f065a6963/
8.PGLBox: Multi-GPU Graph Learning Framework for Web-Scale Recommendation
Link: https://www.aminer.cn/pub/64af9a043fda6d7f065a6a90/
9.Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay
Link: https://www.aminer.cn/pub/64af9a063fda6d7f065a6b9c/
10.Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation
Link: https://www.aminer.cn/pub/64af9a0a3fda6d7f065a702d/
11.Generative Flow Network for Listwise Recommendation
Link: https://www.aminer.cn/pub/64af99fc3fda6d7f065a6275/
12.A Sublinear Time Algorithm for Opinion Optimization in Directed Social Networks via Edge Recommendation
Link: https://www.aminer.cn/pub/64af99fc3fda6d7f065a62a4/
13.Hierarchical Projection Enhanced Multi-behavior Recommendation
Link: https://www.aminer.cn/pub/64af99fe3fda6d7f065a6424/
14.SAMD: An Industrial Framework for Heterogeneous Multi-Scenario Recommendation
Link: https://www.aminer.cn/pub/64af99fe3fda6d7f065a6481/
15.M5: Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation
Link: https://www.aminer.cn/pub/64af9a033fda6d7f065a691f/
16.Modeling Dual Period-Varying Preferences for Takeaway Recommendation
Link: https://www.aminer.cn/pub/64af9a053fda6d7f065a6b8b/
17.Reconsidering Learning Objectives in Unbiased Recommendation: A Distribution Shift Perspective
Link: https://www.aminer.cn/pub/64af9a083fda6d7f065a6db2/
18.Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation
Link: https://www.aminer.cn/pub/64af9a083fda6d7f065a6dd5/
How to use ChatPaper to read literature?
In order to allow more researchers to obtain literature knowledge more efficiently, AMiner developed Chatpaper based on the GLM-130B large model capability to help researchers quickly improve the efficiency of retrieval and reading papers, obtain the latest research trends in the field, and make scientific research work more easily.
ChatPaper is a conversational private knowledge base that integrates retrieval, reading, and knowledge question-and-answer. AMiner hopes that through the power of technology, everyone can acquire knowledge more efficiently.
ChatPaper：https://www.aminer.cn/chat/g
KDD Summit Entrance: https://www.aminer.cn/conf/5ea1b22bedb6e7d53c00c41b/KDD2023