文章來源于機(jī)器學(xué)習(xí)與推薦算法，作者張小磊

?1???摘要

國際頂級(jí)學(xué)術(shù)會(huì)議WWW2020定在2020年4月20-24日于中國臺(tái)灣舉辦。受COVID-19疫情影響(疫情趕緊過去吧)，大會(huì)將在線上舉行。今天是大會(huì)開始的第一天。

本次會(huì)議共收到了1129篇論文投稿，錄用217篇，錄取率僅為19.2%。其中關(guān)于推薦系統(tǒng)的論文大約38篇，推薦系統(tǒng)占比17.5%，可見推薦系統(tǒng)的研究受到學(xué)術(shù)界的廣泛關(guān)注。另外，值得注意的是，接收的推薦系統(tǒng)論文中大部分都是與工業(yè)界合作的產(chǎn)物，因此不管是學(xué)術(shù)界還是工業(yè)界，推薦系統(tǒng)都是研究的熱點(diǎn)與重點(diǎn)。

針對(duì)這38篇論文，我們進(jìn)行了梳理分類，如下表所示

分類數(shù)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

可見，推薦系統(tǒng)應(yīng)用的文章以及序列化推薦的文章占比較大；隨后是提升推薦效率、社會(huì)化推薦、常規(guī)推薦以及利用強(qiáng)化學(xué)習(xí)推薦；其次是興趣點(diǎn)推薦、冷啟動(dòng)問題研究、推薦系統(tǒng)中的安全性、推薦公平性以及可解釋推薦的文章；最后是各有一篇跨域推薦、利用知識(shí)圖推薦、對(duì)話推薦系統(tǒng)以及用于點(diǎn)擊率預(yù)估的推薦。

?2?? 論文列表

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還進(jìn)行了兩場(chǎng)關(guān)于推薦與搜索的Tutorial，分別是利用深度遷移學(xué)習(xí)的搜索與推薦和可信任的推薦與搜索系統(tǒng)，感興趣的小伙伴可以學(xué)習(xí)一下。

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