WWW2020推荐系统论文合集(已分类整理，并提供下载)
文章来源于机器学习与推荐算法，作者张小磊
1 摘要
国际*学术会议WWW2020定在2020年4月20-24日于中国*举办。受COVID-19疫情影响(疫情赶紧过去吧)，大会将在线上举行。今天是大会开始的第一天。
本次会议共收到了1129篇论文投稿，录用217篇，录取率仅为19.2%。其中关于推荐系统的论文大约38篇，推荐系统占比17.5%，可见推荐系统的研究受到学术界的广泛关注。另外，值得注意的是，接收的推荐系统论文中大部分都是与工业界合作的产物，因此不管是学术界还是工业界，推荐系统都是研究的热点与重点。
针对这38篇论文，我们进行了梳理分类，如下表所示
| 分类 | 数量 |
|---|---|
| Practical RS | 6 |
| Sequential RS | 6 |
| Efficient RS |
4 |
| Social RS | 3 |
| General RS |
3 |
| RL for RS |
3 |
| POI RS |
2 |
| Cold Start in RS |
2 |
| Security RS |
2 |
| Fairness RS |
2 |
| Explianability for RS |
2 |
| Cross-domain RS |
1 |
| Knowledge Graph RS |
1 |
| Conversational RS | 1 |
| CTR for RS |
1 |
可见，推荐系统应用的文章以及序列化推荐的文章占比较大；随后是提升推荐效率、社会化推荐、常规推荐以及利用强化学习推荐；其次是兴趣点推荐、冷启动问题研究、推荐系统中的安全性、推荐公平性以及可解释推荐的文章；最后是各有一篇跨域推荐、利用知识图推荐、对话推荐系统以及用于点击率预估的推荐。
2 论文列表
1
Practical RS
Graph Enhanced Representation Learning for News Recommendation
Weakly Supervised Attention for Hashtag Recommendation using Graph Data
Personalized Employee Training Course Recommendation with Career Development Awareness
Understanding User Behavior For Document Recommendation
Recommending Themes for Ad Creative Design via Visual-Linguistic Representations
paper2repo: GitHub Repository Recommendation for Academic Papers
2
Sequential RS
Adaptive Hierarchical Translation-based Sequential Recommendation
Attentive Sequential Model of Latent Intent for Next Item Recommendation
Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation
Intention Modeling from Ordered and Unordered Facets for Sequential Recommendation
Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation
Keywords Generation Improves E-Commerce Session-based Recommendation
3
Efficient RS
Learning to Hash with Graph Neural Networks for Recommender Systems
LightRec: a Memory and Search-Efficient Recommender System
A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems
Efficient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation
4
Social RS
Clustering and Constructing User Coresets to Accelerate Large-scale Top-K Recommender Systems
The Structure of Social Influence in Recommender Networks
Few-Shot Learning for New User Recommendation in Location-based Social Networks
5
Explainability for RS
Directional and Explainable Serendipity Recommendation
Dual Learning for Explainable Recommendation: Towards Unifying User Preference Prediction and Review Generation
6
POI RS
Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices
A Category-Aware Deep Model for Successive POI Recommendation on Sparse Check-in Data
7
General RS
Efficient Neural Interaction Function Search for Collaborative Filtering
Learning the Structure of Auto-Encoding Recommenders
Deep Global and Local Generative Model for Recommendation
8
Fairness in RS
Hierarchical Visual-aware Minimax Ranking Based on Co-purchase Data for Personalized Recommendation
FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms
9
RL for RS
Off-policy Learning in Two-stage Recommender Systems
Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation
10
Cross-domain RS
Exploiting Aesthetic Preference in Deep Cross Networks for Cross-domain Recommendation
11
Knowledge Graph RS
Reinforced Negative Sampling over Knowledge Graph for Recommendation
12
Conversational RS
Latent Linear Critiquing for Conversational Recommender Systems
13
CTR for RS
Adversarial Multimodal Representation Learning for Click-Through Rate Prediction
3 官方Tutorial
最后，WWW2020还进行了两场关于推荐与搜索的Tutorial，分别是利用深度迁移学习的搜索与推荐和可信任的推荐与搜索系统，感兴趣的小伙伴可以学习一下。
获取以上WWW2020推荐系统论文，请关注机器学习与推荐算法公众号后台回复【0420】即可。
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