嘿，记得给“机器学习与推荐算法”添加星标

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第28届SIGKDD会议将于8月14日至18日在华盛顿举行。据统计，今年共有1695篇有效投稿，其中254篇论文被接收，接收率为14.98%，相比KDD2021的接收率15.44%有所下降。其中，涉及到的推荐系统相关的论文共24篇（本次只整理了Research Track相关论文，对于Applied Data Science Track读者可自行前往下文链接查看）。整理不易，欢迎小手点个在看/分享。

往年KDD推荐系统论文整理可参考：

KDD2021推荐系统论文集锦

KDD2020 推荐系统论文一览（可下载）

本公众号一如既往的收集与整理了发表在该会议上的推荐系统相关论文，以供研究者们提前一睹为快。本会议接受的论文主要整理了Research Track Papers，因此大家可以提前领略和关注学术界的最新动态。如果不放心本文整理的推荐系统论文集锦，也可自行前往官网查看，学术类论文官网接收论文列表如下：

https://kdd.org/kdd2022/paperRT.html

应用类论文接收列表如下：

https://kdd.org/kdd2022/paperADS.html

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Research Track Papers

通过对本次接收的论文进行总结发现，从所涉及的研究主题角度来看，此次大会主要聚焦在了推荐系统中的公平性[1]、Bias问题[9,10,12,17,23]、对话推荐系统[8,15]、推荐中的隐私和安全问题[6,9]、推荐系统的特征交叉[3]、可解释推荐系统[13]、捆绑推荐[14]、基于图的推荐系统[2,11,18,19,24]、轻量化推荐模型[24]、点击率预估问题[22,23]、序列推荐模型[7,10,13]等，与去年所关注的主题类似；

从推荐技术的角度来看，包括对于经典协同过滤的改进[20,21]、基于对抗学习的推荐算法[22]、基于元学习的推荐系统[1]、基于强化学习的推荐算法[15,24]、基于提示学习的推荐算法[8]、基于注意力机制的推荐算法[13,17]、基于对比学习的推荐算法[14]、基于Transformer的推荐算法[18,19]等。

• [1] Comprehensive Fair Meta-learned Recommender System

• [2] Graph-Flashback Network for Next Location Recommendation

• [3] Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems

• [4] Practical Counterfactual Policy Learning for Top-K Recommendations

• [5] Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis

• [6] Knowledge-enhanced Black-box Attacks for Recommendations

• [7] Towards Universal Sequence Representation Learning for Recommender Systems

• [8] Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning

• [9] Debiasing Learning for Membership Inference Attacks Against Recommender Systems

• [10] Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers

• [11] User-Event Graph Embedding Learning for Context-Aware Recommendation

• [12] Invariant Preference Learning for General Debiasing in Recommendation

• [13] PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions

• [14] CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation

• [15] Learning Relevant Information in Conversational Search and Recommendation using Deep Reinforcement Learning

• [16] MDP2 Forest: A Constrained Continuous Multi-dimensional Policy Optimization Approach for Short-video Recommendation

• [17] Counteracting User Attention Bias in Music Streaming Recommendation via Reward Modification

• [18] Multi-Behavior Hypergraph-Enhanced Transformer for Next-Item Recommendation

• [19] Self-Augmented Hypergraph Transformer for Recommender Systems

• [20] Towards Representation Alignment and Uniformity in Collaborative Filtering

• [21] HICF: Hyperbolic Informative Collaborative Filtering

• [22] Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction

• [23] A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction

• [24] Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation

上述部分论文的pdf版本我们已经整理成了合集，大家可以在后台回复【KDD2022】获取下载链接，尽享论文盛宴吧~

[1] Comprehensive Fair Meta-learned Recommender System

Tianxin Wei (University of Illinois Urbana Champaign)*; Jingrui He (University of Illinois at Urbana-Champaign)

https://arxiv.org/abs/2206.04789

In recommender systems, one common challenge is the cold-start problem, where interactions are very limited for fresh users in the systems. To address this challenge, recently, many works introduce the meta-optimization idea into the recommendation scenarios, i.e. learning to learn the user preference by only a few past interaction items. The core idea is to learn global shared meta-initialization parameters for all users and rapidly adapt them into local parameters for each user respectively. They aim at deriving general knowledge across preference learning of various users, so as to rapidly adapt to the future new user with the learned prior and a small amount of training data. However, previous works have shown that recommender systems are generally vulnerable to bias and unfairness. Despite the success of meta-learning at improving the recommendation performance with cold-start, the fairness issues are largely overlooked. In this paper, we propose a comprehensive fair meta-learning framework, named CLOVER, for ensuring the fairness of meta-learned recommendation models. We systematically study three kinds of fairness - individual fairness, counterfactual fairness, and group fairness in the recommender systems, and propose to satisfy all three kinds via a multi-task adversarial learning scheme. Our framework offers a generic training paradigm that is applicable to different meta-learned recommender systems. We demonstrate the effectiveness of CLOVER on the representative meta-learned user preference estimator on three real-world data sets. Empirical results show that CLOVER achieves comprehensive fairness without deteriorating the overall cold-start recommendation pe