yehjames / awesome-rec-papers

整理推薦系統必讀papers

awesome-rec-papers

整理推薦系統必讀papers

必讀

1. [Youtube] Deep Neural Networks for YouTube Recommendations (Youtube 2016)

2. [Airbnb] Applying Deep Learning To Airbnb Search (Airbnb 2018)

3. [Pinterest] Personalized content blending In the Pinterest home feed (Pinterest 2016)

4. [Airbnb] Search Ranking and Personalization at Airbnb Slides (Airbnb 2018)

5. [Word2Vec] Distributed Representations of Words and Phrases and their Compositionality (Google 2013)

6. [Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb (Airbnb 2018)

7. [Alibaba Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (Alibaba 2018)

8. [Word2Vec] Efficient Estimation of Word Representations in Vector Space (Google 2013)

9. [LINE] LINE - Large-scale Information Network Embedding (MSRA 2015)

10. [DCN] Deep & Cross Network for Ad Click Predictions (Stanford 2017)

11. [Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features (Microsoft 2016)

Conference

1、與推薦系統直接相關的會議

RecSys -The ACM Conference Series on Recommender Systems.

2、數據挖掘相關的會議

SIGKDD - The ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

WSDM - The International Conference on Web Search and Data Mining.

ICDM - The IEEE International Conference on Data Mining.

SDM -TheSIAM International Conference on Data Mining.

ECML-PKDD - The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases

3、機器學習相關的會議

ICML - The International Conference on Machine Learning.

NIPS - The Conference on Neural Information Processing Systems

4、信息檢索相關的會議

SIGIR - The ACM International Conference on Research and Development in Information Retrieval

5、數據庫相關的會議

CIKM - The ACM International Conference on Information and Knowledge Management.

6、Web相關的會議

WWW - The International World Wide Web Conference.

7、人工智慧相關的會議

AAAI - The National Conference of the American Association for Artificial Intelligence.

IJCAI - The International Joint Conference on Artificial Intelligence.

ECAI -European Conference on Artificial Intelligence

UAI - The Conference on Uncertainty in Artificial Intelligence

大神

Yehuda Koren 個人主頁：Koren's HomePage

主要貢獻：Netflix Prize的冠軍隊成員，是推薦系統領域的大神級人物，曾就職雅虎，現就職於谷歌

代表文獻：Matrix Factorization Techniques For Recommender Systems

Steffen Rendle 個人主頁：Rendle's HomePage

主要貢獻：經典推薦演算法FM和BPR的提出者，現就職於谷歌

代表文獻：BPR: Bayesian Personalized Ranking from Implicit Feedback

Hao Ma 個人主頁：HaoMa's HomePage

主要貢獻：社會化推薦領域的大牛，提出了許多基於社會化推薦的有效演算法，現就職於微軟

代表文獻：SoRec: Social Recommendation Using Probabilistic Matrix Factorization

Julian McAuley 個人主頁：McAuley

主要貢獻：研究方向為社交網路、數據挖掘、推薦系統，現為加利福尼亞大學聖迭戈分校助理教授

代表文獻：Leveraging social connections to improve personalized ranking for collaborative filtering

郭貴冰 個人主頁：Guibing Guo's HomePage

主要貢獻：國內推薦系統大牛，創辦了推薦系統開源項目LibRec

代表文獻：TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings

Hao Wang 個人主頁：HaoWang's HomePage

主要貢獻：擅長運用深度學習技術提高推薦系統性能

代表文獻：Collaborative deep learning for recommender systems

何向南 個人主頁：Xiangnan He's Homepage

主要貢獻：運用深度學習技術提高推薦系統性能

代表文獻：Neural Collaborative Filtering

Robin Burke 個人主頁：rburke's HomePage

主要貢獻：混合推薦方向的大牛

代表文獻：Hybrid recommender systems: Survey and experiments

項亮 主要貢獻：國內推薦系統領域中理論與實踐並重的專家，Netflix Prize第二名

代表文獻：《推薦系統實踐》。

謝幸 個人主頁：Xing's Page

主要貢獻：專註於數據挖掘、社會計算等，擅長可解釋性推薦研究等。

代表文獻：A Survey on Knowledge Graph-Based Recommender Systems

Jiliang Tang 個人主頁：Jiliang's Page

主要貢獻：擅長利用社交網路分析相關技術提升推薦性能。

代表文獻：Social Recommendation: A Review

趙鑫 個人主頁：zhaoxin's HomePage

主要貢獻：國內推薦系統著名學者，側重利用自然語言處理技術來提升Top-N推薦性能

代表文獻：Improving Sequential Recommendation with Knowledge-enhanced Memory Networks

石川 個人主頁：shichuan's HomePage

主要貢獻：研究方向為異質信息網路上的推薦，提出了加權的異質信息相似度計算等

代表文獻：Semantic Path based Personalized Recommendation on Weighted Heterogeneous Information Networks

吳樂 個人主頁：Wu Le's HomePage

主要貢獻：研究方向為結合社交信息的推薦，提出了神經影響擴散模型等

代表文獻：A Neural Influence Diffusion Model for Social Recommendation

王鴻偉 個人主頁：Hongwei's Page

主要貢獻：關註於圖機器學習，聚焦在結合知識圖譜來進行推薦的領域。

代表文獻：Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation

參考來源：

1. 王喆 - 推荐系统论文、学习资料、业界分享

2. Must-read papers on Recommender System

3. 知乎 - 推荐系统干货总结