WSDM2022推荐算法部分论文整理（附直播课程）

推荐系统作为AI最成功和最重要的应用之一，帮助消费者更容易地找到相关的或感兴趣的内容、商品或者服务。

对于想学习推荐系统的同学，我整理了WSDM2022最新的包括了序列推荐、跨域推荐、去偏推荐、联邦推荐等13篇推荐系统论文，供大家提前领略学术前沿趋势与牛人的最新想法。完整的WSDM2022推荐算法整理可移步WSDM2022推荐系统论文集锦。

跨域推荐

01.RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation

02.Personalized Transfer of User Preferences for Cross-domain Recommendation

序列推荐

03.Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation

04.Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation

去偏推荐

05.It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences are Dynamic

06.Fighting Mainstream Bias in Recommender Systems via Local Fine Tuning

联邦推荐

07.PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion

基于图结构的推荐

08.Profiling the Design Space for Graph Neural Networks based Collaborative Filtering

09.Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation

公平性推荐

10.Enumerating Fair Packages for Group Recommendations

基于元学习的推荐

11.Long Short-Term Temporal Meta-learning in Online Recommendation

基于强化学习的推荐

12.A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising

13.Choosing the Best of All Worlds: Accurate, Diverse, and Novel Recommendations through Multi-Objective Reinforcement Learning

那么多推荐算法论文，需要怎么杨去精读和泛读呢，为了能让你对推荐系统中的各个部分的算法有一个总体的认识，我推荐你来听Pirtuo老师的《欢迎来到推荐的世界》直播课。

—— 老师介绍 ——

—— 直播大纲 ——

7月21日 晚上20：00

• 开营

• 推荐系统概述

• 召回算法概述

• 排序算法概述

• 论文泛读

• 学习路径

7月22日 晚上20：00

• INTRODUCTION

• RECOMMENDER SYSTEM OVERVIEW

• WIDE & DEEP LEARNING

• SYSTEM IMPLEMENTATION

• EXPERIMENT RESULTS

支付0.1元领取直播

已预约的同学，还额外附赠2022推荐系统paper13篇~