推薦係統,是指根據用戶的習慣、偏好或興趣,從不斷到來的大規模信息中識別滿足用戶興趣的信息的過程。推薦推薦任務中的信息往往稱為物品(Item)。根據具體應用背景的不同,這些物品可以是新聞、電影、音樂、廣告、商品等各種對象。推薦係統利用電子商務網站向客戶提供商品信息和建議,幫助用戶決定應該購買什麼產品,模擬銷售人員幫助客戶完成購買過程。個性化推薦是根據用戶的興趣特點和購買行為,向用戶推薦用戶感興趣的信息和商品。隨著電子商務規模的不斷擴大,商品個數和種類快速增長,顧客需要花費大量的時間才能找到自己想買的商品。這種瀏覽大量無關的信息和產品過程無疑會使淹沒在信息過載問題中的消費者不斷流失。為了解決這些問題,個性化推薦係統應運而生。個性化推薦係統是建立在海量數據挖掘基礎上的一種高級商務智能平台,以幫助電子商務網站為其顧客購物提供完全個性化的決策支持和信息服務。

知識薈萃

入門學習

  1. 探索推薦引擎內部的秘密,第 1 部分 推薦引擎初探 IBM developerWorks

  2. 探索推薦引擎內部的秘密,第 2 部分 深入推薦引擎相關算法 - 協同過濾

  3. 探索推薦引擎內部的秘密,第 3 部分 深入推薦引擎相關算法 - 聚類

  4. 項亮《推薦係統實踐》筆記(1,2)

  5. 推薦算法綜述(一,二,三,四,五)

  6. 推薦係統,第一部分 方法和算法簡介 第 2 部分 開源引擎簡介

  7. 深度學習在推薦係統中的一些應用

  8. 《紐約時報》如何打造新一代推薦係統

  9. 深度學習在推薦算法上的應用進展

  10. 如何學習推薦係統? by 知乎

  11. 了解關於係統推薦算法的知識,有什麼好的資源推薦? by 知乎

  12. 項亮_推薦係統_博士論文.pdf

  13. 微信公眾號:resyschina 中國最專業的個性化推薦技術與產品社區。

  14. 餓了麼推薦係統:從0到1

  15. 【直播回顧】21天搭建推薦係統:實現“千人千麵”個性化推薦(含視頻)

  16. 這本書收錄了推薦係統很多經典論文,話題涵蓋非常廣,第三章專門講內容推薦的基本原理,第九章是一個具體的基於內容推薦係統的案例。 - 2010

    https://book.douban.com/subject/3695850/

  17. Deep Learning Meets Recommendation Systems by Wann-Jiun.https://blog.nycdatascience.com/student-works/deep-learning-meets-recommendation-systems/

  18. Machine Learning for Recommender systems Source:https://medium.com/recombee-blog/machine-learning-for-recommender-systems-part-1-algorithms-evaluation-and-cold-start-6f696683d0ed

  19. Check out our new client-side integration support and deploy personalized recommendations faster

    https://medium.com/recombee-blog/check-out-our-new-client-side-integration-support-and-deploy-personalized-recommendations-faster-7dd7bf5b6241

  20. Practical Recommender Systems by Kim Falk (Manning Publications). Chapter 1

    https://www.manning.com/books/practical-recommender-systems

  21. Recommender Systems Handbook by Ricci, F. et al.

    https://dl.acm.org/citation.cfm?id=1941884

綜述

  1. Deep Learning based Recommender System: A Survey and New Perspectives 用於推薦係統的所有深度學習方法

    [https://arxiv.org/pdf/1707.07435.pdf]

  2. Toward the next generation of recommender systems:A survey of the state-of-the-art and possiblie extensions (2005), Adomavicius G, Tuzhilin A.http://people.stern.nyu.edu/atuzhili/pdf/TKDE-Paper-as-Printed.pdf

  3. Recommender systems: an introduction (2011), Zanker M, Felfernig A, Friedrich G.

    http://recommenderbook.net/media/szeged.pdf

  4. 推薦係統調研報告及綜述-張永鋒

    http://yongfeng.me/attach/rs-survey-zhang.pdf

  5. 綜述論文合集-hongleizhang 2002-2019

    https://github.com/hongleizhang/RSPapers/tree/master/01-Surveys

  6. 知識圖譜的推薦係統綜述

    http://html.rhhz.net/tis/html/201805001.htm

  7. Recommender-System論文、學習資料以及業界分享

    https://github.com/zhaozhiyong19890102/Recommender-System

  8. RecommenderSystem-paper/Survey - daicoolb

    https://github.com/daicoolb/RecommenderSystem-Paper/tree/master/Survey

進階文章

1997

  1. Recommender system (1997), P Resnick, HR Varian.

1998

  1. Empirical analysis of predictive algorithms for collaborative filtering (1998), John S Breese, David Heckerman, Carl M Kadie.
    [http://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-12.pdf]
  2. Clustering methods for collaborative filtering (1998), Ungar, L. H., D. P. Foster.
    [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.44.7783&rep=rep1&type=pdf]

1999

  1. A bayesian model for collaborative filtering (1999),Chien Y H, George E I.
    [http://www-stat.wharton.upenn.edu/~edgeorge/Research_papers/Bcollab.pdf]
  2. Using probabilistic relational models for collaborative filtering (1999), Lise Getoor, Mehran Sahami [http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=52BCC5212B0117CBB8BA48A1D8230E30?doi=10.1.1.40.4507&rep=rep1&type=pdf]

2001

  1. Item-based Collaborative Filtering Recommendation Algorithms (2001), Badrul M Sarwar, George Karypis, Joseph A Konstan, John Riedl. [http://www10.org/cdrom/papers/pdf/p519.pdf]

2002

  1. Hybrid recommender systems: Survey and experiments (2002), Burke R. [https://www.researchgate.net/profile/Robin_Burke/publication/263377228_Hybrid_Recommender_Systems_Survey_and_Experiments/links/5464ddc20cf2f5eb17ff3149.pdf]

2003

  1. Amazon Recommendations Item-to-Item Collaborative Filtering (2003), G Linden, B Smith, et al.
    [http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf]

2004

  1. A maximum entropy approach for collaborative filtering (2004), Browning J, Miller D J.
    [http://www.yaroslavvb.com/papers/browning-maximum.pdf]
  2. Supporting user query relaxation in a recommender system (2004),Mirzadeh N, Ricci F, Bansal M. [https://www.researchgate.net/profile/Francesco_Ricci5/publication/221017551_Supporting_User_Query_Relaxation_in_a_Recommender_System/links/0deec524dcde30df0d000000.pdf]

2005

  1. Case-based recommender systems: a unifying view.Intelligent Techniques for Web Personalization (2005),Lorenzi F, Ricci F. [www.inf.unibz.it/~ricci//papers/LorenziRicciCameraReady.pdf]
  2. SVD-based collaborative filtering with privacy (2005), Polat H, Du W.
    [http://www.cis.syr.edu/~wedu/Research/paper/sac2004.pdf]

2007

  1. Improving regularized singular value decomposition for collaborative filtering (2007), A Paterek.
    [http://www.mimuw.edu.pl/~paterek/ap_kdd.pdf]
  2. Predicting Clicks Estimating the click-through rate for new ads (2007),M Richardson, E Dominowska.
    [http://research.microsoft.com/en-us/um/people/mattri/papers/www2007/predictingclicks.pdf]
  3. Restricted Boltzmann Machines for Collaborative Filtering (2007),R Salakhutdinov, A Mnih, G Hinton. [http://machinelearning.wustl.edu/mlpapers/paper_files/icml2007_SalakhutdinovMH07.pdf]

2008

  1. Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo (2008),R Salakhutdinov, et al.
    [http://www.cs.utoronto.ca/~amnih/papers/bpmf.pdf]
  2. Factorization Meets the Neighborhood- a Multifaceted Collaborative Filtering Model (2008),Y Koren. [http://www.academia.edu/download/35945687/Factorization_meets_the_neighborhood_a_multifaceted_collaborative_filtering_model.pdf]

2009

  1. Utility-based repair of inconsistent requirements (2009), Felfernig A, Mairitsch M, Mandl M, et al.
    [http://link.springer.com/content/pdf/10.1007/978-3-642-02568-6_17.pdf]
  2. Bayesian Personalized Ranking from Implicit Feedback (2009), S Rendle, C Freudenthaler, Z Gantner.
    [https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf]
  3. Fast computation of query relaxations for knowledge-based recommenders (2009),Jannach D.
    [http://ls13-www.cs.tu-dortmund.de/homepage/publications/jannach/Journal_AICOM09.pdf]
  4. A hybrid approach to item recommendation in folksonomies (2009), Wetzker R, Umbrath W, Said A.
    [http://www.dai-labor.de/fileadmin/Files/Publikationen/Buchdatei/wetzker_folksonomyrecommendation_esair2009_final.pdf]

2010

  1. Click-Through Rate Estimation for Rare Events in Online Advertising (2010),X Wang, W Li, Y Cui, R Zhang.
    [http://www.cs.cmu.edu/~./xuerui/papers/ctr.pdf]
  2. Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine (2010), T Graepel, JQ Candela.
    [http://machinelearning.wustl.edu/mlpapers/paper_files/icml2010_GraepelCBH10.pdf]
  3. Rendle S, Schmidt-Thieme L. Pairwise interaction tensor factorization for personalized tag recommendation[C]//Proceedings of the third ACM international conference on Web search and data mining. ACM, 2010: 81-90.
    [https://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle2010-PITF.pdf]
  4. Factor in the Neighbors- Scalable and Accurate Collaborative Filtering (2010), Y Koren.
    [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.4158&rep=rep1&type=pdf]

2011

  1. Tag-aware recommender systems: a state-of-the-art survey (2011), Zhang Z K, Zhou T, Zhang Y C.
    [http://arxiv.org/pdf/1202.5820.pdf]
  2. Feature-Based Matrix Factorization (2011), T Chen, Z Zheng, Q Lu, W Zhang, Y Yu.
    [https://arxiv.org/pdf/1109.2271.pdf?ref=theredish.com/web)]

2012

  1. A Two-Stage Ensemble of Diverse Models for Advertisement Ranking in KDD Cup 2012 (2012),KW Wu, CS Ferng, CH Ho, AC Liang, CH Huang. [http://ntur.lib.ntu.edu.tw/retrieve/188498/03.pdf]
  2. Combining Factorization Model and Additive Forest for Collaborative Followee Recommendation (2012), T Chen, L Tang, Q Liu, D Yang, S Xie, X Cao, C Wu.
    [http://curtis.ml.cmu.edu/w/courses/images/4/4e/AdditiveForestChen.pdf]
  3. Rendle, Steffen. "Factorization machines with libfm."ACM Transactions on Intelligent Systems and Technology (TIST)3.3 (2012): 57. [http://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf]
  4. Factorization Machines with libFM (2012),S Rendle.
    [http://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf]
  5. Rendle S. Factorization machines with libfm[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2012, 3(3): 57. [http://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf]
  6. Ensemble of Collaborative Filtering and Feature Engineered Models for Click Through Rate Prediction (2012), M Jahrer, A Toscher, JY Lee, J Deng.
    [https://pdfs.semanticscholar.org/eeb9/34178ea9320c77852eb89633e14277da41d8.pdf]

2013

  1. Van den Oord A, Dieleman S, Schrauwen B. Deep content-based music recommendation[C]//Advances in neural information processing systems. 2013: 2643-2651.
    [http://papers.nips.cc/paper/5004-deep-content-based-music-recommendation.pdf]
  2. Deep content-based music recommendation (2013), A Van den Oord, S Dieleman.
    [http://papers.nips.cc/paper/5004-deep-content-based-music-recommendation.pdf]
  3. A Hybrid Approach with Collaborative Filtering for Recommender Systems (2013), G Badaro, H Hajj, et al.
    [http://staff.aub.edu.lb/~we07/Publications/A%20Hybrid%20Approach%20with%20Collaborative%20Filtering%20for%20Recommender%20Systems.pdf]

2014

  1. Zhang T, Zhang T, Zhang T, et al. Gradient boosting factorization machines[C]// ACM Conference on Recommender Systems. ACM, 2014:265-272.
    [http://pdfs.semanticscholar.org/cd57/9e1e9cc350c3f7746e6ae6911a97e21ba27c.pdf]
  2. Practical Lessons from Predicting Clicks on Ads at Facebook(2014), X He, J Pan, O Jin, T Xu, B Liu, T Xu, Y Shi.
    [http://quinonero.net/Publications/predicting-clicks-facebook.pdf]

2015

  1. Simple and scalable response prediction for display advertising (2015),O Chapelle, E Manavoglu, R Rosales. [http://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]
  2. AutoRec- Autoencoders Meet Collaborative Filtering (2015), Suvash Sedhain, Aditya Krishna Menon, et al.
    [http://users.cecs.anu.edu.au/~u5098633/papers/www15.pdf]
  3. Collaborative Deep Learning for Recommender Systems (2015), Hao Wang, N Wang, Dityan Yeung.
    [http://www.wanghao.in/mis/CDL.pdf]

2016

  1. Juan Y, Zhuang Y, Chin W S, et al. Field-aware factorization machines for CTR prediction[C]//Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016: 43-50.
    [http://ntucsu.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf]
  2. Zhang W, Du T, Wang J, et al. Deep Learning over Multi-field Categorical Data[C]. european conference on information retrieval, 2016: 45-57. [https://arxiv.org/abs/1601.02376]
  3. Factorization Meets the Item Embedding- Regularizing Matrix Factorization with Item Co-occurrence (2016), D Liang, J Altosaar, L Charlin, DM Blei.
    [https://pdfs.semanticscholar.org/f14f/c33e0a351dff4f4e02510276604a93d1b9fa.pdf]
  4. F2M Scalable Field-Aware Factorization Machines (2016),C Ma, Y Liao, Y Wang, Z Xiao. [https://pdfs.semanticscholar.org/bb29/9887ba700300757de7560dc34b48b127cdca.pdf]
  5. Blondel M, Fujino A, Ueda N, et al. Higher-order factorization machines[C]//Advances in Neural Information Processing Systems. 2016: 3351-3359. [http://papers.nips.cc/paper/6144-higher-order-factorization-machines.pdf]
  6. Shan Y, Hoens T R, Jiao J, et al. Deep Crossing: Web-scale modeling without manually crafted combinatorial features[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016: 255-262.
    [www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf]
  7. Chen J, Sun B, Li H, et al. Deep ctr prediction in display advertising[C]//Proceedings of the 2016 ACM on Multimedia Conference. ACM, 2016: 811-820.
    [https://arxiv.org/pdf/1609.06018.pdf]
  8. Hybrid Collaborative Filtering with Autoencoders (2016), F Strub, J Mary, R Gaudel.
    [https://arxiv.org/pdf/1603.00806)]
  9. Wide & Deep Learning for Recommender Systems (2016),HT Cheng, L Koc, J Harmsen, T Shaked.
    [https://arxiv.org/pdf/1606.07792)]
  10. Deep Neural Networks for YouTube Recommendations (2016), Paul Covington, Jay Adams, Emre Sargin. [https://www.researchgate.net/publication/307573656_Deep_Neural_Networks_for_YouTube_Recommendations)]

2017

  1. He X, Chua T S. Neural Factorization Machines for Sparse Predictive Analytics[J]. 2017:355-364.
    [https://arxiv.org/pdf/1708.05027.pdf]
  2. Ning Y, Shi Y, Hong L, et al. A Gradient-based Adaptive Learning Framework for E icient Personal Recommendation[J]. 2017. [http://people.cs.vt.edu/naren/papers/recs254-ningA.pdf]
  3. Qu Y, Cai H, Ren K, et al. Product-Based Neural Networks for User Response Prediction[C]// IEEE, International Conference on Data Mining. IEEE, 2017:1149-1154.
    [https://arxiv.org/pdf/1611.00144.pdf]
  4. Guo H, Tang R, Ye Y, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction[C]// Twenty-Sixth International Joint Conference on Artificial Intelligence. 2017:1725-1731.
    [https://arxiv.org/pdf/1703.04247.pdf]
  5. Xiao J, Ye H, He X, et al. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks[J]. 2017. [https://ru.arxiv.org/pdf/1708.04617.pdf]
  6. A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems (2017),Xin Dong, Lei Yu, Zhonghuo Wu, Yuxia Sun, Lingfeng Yuan, Fangxi Zhang.
    [http://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14676/13916)]
  7. Collaborative Deep Embedding via Dual Networks (2017), Yilei Xiong, Dahua Lin, et al.
    [https://openreview.net/pdf?id=r1w7Jdqxl)]
  8. Recurrent Recommender Networks (2017), Chao-Yuan Wu.
    [http://delivery.acm.org/10.1145/3020000/3018689/p495-wu.pdf?ip=221.226.125.130&id=3018689&acc=OA&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E5945DC2EABF3343C&CFID=995126498&CFTOKEN=96329132&acm=1508034746_8da751768f4ee19af912968914bbbaa6)_]

2018

2019

Tutorial

  1. Tutorial: Recommender Systems IJCAI 2013

    [http://ijcai13.org/files/tutorial_slides/td3.pdf]

  2. Tutorial: Context In Recommender Systems 2016

    [https://www.slideshare.net/irecsys/tutorial-context-in-recommender-systems]

  3. 融合用戶上下文的個性化推薦 張敏, 清華大學

    [http://www.cips-smp.org/smp2017/public/workshop-recommendation.html]

  4. 今日頭條的人工智能技術實踐 曹歡歡博士

    [http://www.cips-smp.org/smp2017/public/workshop-recommendation.html]

  5. 基於循環神經網絡的序列推薦 吳書

    [http://www.cips-smp.org/smp2017/public/workshop-recommendation.html]

  6. 冷啟動推薦的思考與進展 趙鑫

    [http://www.cips-smp.org/smp2017/public/workshop-recommendation.html]

  7. Recommender Systems: A Brief Introduction 中科大 劉淇 [http://home.ustc.edu.cn/~zengxy/dm/courseware/A%20brief%20introduction%20to%20RecSys.pdf]

  8. Deep Learning for Recommender Systems by Balázs Hidasi.RecSys Summer School, 21-25 August, 2017, Bozen-Bolzano.

https://www.slideshare.net/balazshidasi/deep-learning-in-recommender-systems-recsys-summer-school-2017

  1. Deep Learning for Recommender Systems by Alexandros Karatzoglou and Balázs Hidasi. RecSys2017 Tutorial.

    https://www.slideshare.net/kerveros99/deep-learning-for-recommender-systems-recsys2017-tutorial

  2. Introduction to recommender Systems by Miguel González-Fierro.

    https://github.com/miguelgfierro/sciblog_support/blob/master/Intro_to_Recommendation_Systems/Intro_Recommender.ipynb

  3. Collaborative Filtering using a RBM by Big Data University.

    https://github.com/santipuch590/deeplearning-tf/blob/master/dl_tf_BDU/4.RBM/ML0120EN-4.2-Review-CollaborativeFilteringwithRBM.ipynb

  4. Building a Recommendation System in TensorFlow: Overview.

    https://cloud.google.com/solutions/machine-learning/recommendation-system-tensorflow-overview

視頻教程

  1. 如何設計一個推薦係統

    [https://www.youtube.com/watch?v=MZkxusQ6GNo]

  2. Recommender Systems | Coursera [https://www.coursera.org/specializations/recomender-systems]

  3. 大數據推薦係統算法視頻教程

    https://pan.baidu.com/s/1U89CR_ZH_1JzsPOOKLbMyQ%E8%AF%B7%E6%B7%BB%E5%8A%A0%E9%93%BE%E6%8E%A5%E6%8F%8F%E8%BF%B0

    提取碼:5ipq

  4. Introduction to Recommender Systems

    https://www.classcentral.com/course/recsys-1029

代碼

  1. annoy - Approximate Nearest Neighbors in Python optimized for memory usage. [https://github.com/spotify/annoy]

  2. fastFM - A library for Factorization Machines. [https://github.com/ibayer/fastFM]

  3. implicit - A fast Python implementation of collaborative filtering for implicit datasets. [https://github.com/benfred/implicit]

  4. libffm- A library for Field-aware Factorization Machine (FFM). [https://github.com/guestwalk/libffm]

  5. LightFM - A Python implementation of a number of popular recommendation algorithms.

    [https://github.com/lyst/lightfm]

  6. surprise - A scikit for building and analyzing recommender systems. [http://surpriselib.com]

  7. Crab- a python recommender based on the popular packages NumPy, SciPy, matplotlib. The main repository seems to be abandoned.

    [http://muricoca.github.io/crab/]

  8. RecQ

    https://github.com/hongleizhang/RecQ

  9. Recommender System Suits: An open source toolkit for recommender system

    https://github.com/hongleizhang/RSAlgorithms

  10. Surprise- is a Python scikit building and analyzing recommender systems.

    https://github.com/NicolasHug/Surprise

  11. SpotLight- Spotlight uses PyTorch to build both deep and shallow recommender models.

    https://github.com/maciejkula/spotlight

  12. Python-Recsys: A python library for implementing a recommender system.

    https://github.com/ocelma/python-recsys

  13. LibRec- A java library for the state-of-the-art algorithms in recommeder sytem.

    https://www.librec.net/

  14. SparkMovieLens- A scalable on-line movie recommender using Spark and Flask.

    https://github.com/jadianes/spark-movie-lens

  15. Elasticsearch- Building a Recommender with Apache Spark & Elasticsearch

    https://github.com/IBM/elasticsearch-spark-recommender

相關會議

  • KDDthe community for data mining, data science and analytics.
  • AAAIpromotes research in, and responsible use of, artificial intelligence.
  • WWWprovides the world a premier forum for discussion and debate about the evolution of the Web, the standardization of its associated technologies, and the impact of those technologies on society and culture.
  • MMis the premier international conference in the area of multimedia within the field of computer science. Multimedia research focuses on integration of the multiple perspectives offered by different digital modalities including images, text, video, music, sensor data, spoken audio.
  • NIPShas a responsibility to provide an inclusive and welcoming environment for everyone in the fields of AI and machine learning.
  • ICMLis the leading international machine learning conference and is supported by the International Machine Learning Society (IMLS).
  • CIKMprovides an international forum for presentation and discussion of research on information and knowledge management, as well as recent advances on data and knowledge bases.
  • SIGIRis the Association for Computing Machinery’s Special Interest Group on Information Retrieval. Since 1963, we have promoted research, development and education in the area of search and other information access technologies.
  • Recsysis the most famous conference in recommender system.
  • WSDM(pronounced "wisdom") is one of the the premier conferences on web inspired research involving search and data mining.
  • ICDMdraws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high performance computing.

領域專家

  1. 陳恩紅

    從中國科技術大學教授,多媒體計算與通信教育部-微軟重點實驗室副主任。機器學習與數據挖掘、網絡信息處理領的專家,相關研究獲得國家傑出青年科學基金、教育部新世紀優秀人才計劃等資助。曾擔任KD、AAAI2012、ICDM、PAKDD、SDM3等30餘個國際學術會議的程序委員。CCF理事、人工智能與模式識別專委會委員、數據庫專委會委員、大數據專家委員會委員,中國人工智能學會理事,知識工程與分布智能專業委員會副主任委員、IEEE高級會員。 [http://staff.ustc.edu.cn/~cheneh/]

  2. 唐傑

    清華大學計算機係副教授、博士生導師。主要研究興趣包括:社會網絡分析、數據挖掘、機器學習和語義Web。研發了研究者社會網絡ArnetMiner係統,吸引全球220個國家和地區432萬獨立IP的訪問。榮獲首屆國家自然科學基金優秀青年基金,2012中國計算機學會青年科學家獎、2010年清華大學學術新人獎(清華大學40歲以下教師學術最高獎)、2011年北京市科技新星、IBM全球創新教師獎以及KDD’12 Best Poster Award、PKDD’11 Best Student Paper Runnerup和JCDL’12 Best Student Paper Nomination。 [http://keg.cs.tsinghua.edu.cn/jietang/]

  3. 張敏

    清華大學計算機科學與技術係副教授,博士生導師。主要研究領域為信息檢索、個性化推薦、用戶畫像與建模、用戶行為分析。現任智能技術與係統國家重點實驗中心實驗室科研副主任、網絡與媒體技術教育部-微軟重點實驗室副主任。在重要的國際期刊和會議上發表多篇學術論文,包括JIR、IJCAI、SIGIR、WWW、CIKM、WSDM等,Google Scholar引用約2500次。已授權專利11項。擔任重要國際期刊TOIS編委,國際會議WSDM 2017和AIRS2016程序委員會主席,SIGIR 2018 short paper主席, WWW,SIGIR,CIKM,WSDM等重要國際會議的領域主席或資深審稿人。現任中國中文信息學會理事,中國計算機學會高級會員。http://www.thuir.org/group/~mzhang/~

  4. 謝幸

    微軟亞洲研究院首席研究院,中國科學技術大學簡直博士生導師。研究方向為數據挖掘、社會計算、普適計算。謝幸博士於2001年7月加入微軟亞洲研究院,現任首席研究員,中國科技大學兼職博士生導師,以及微軟-中科大聯合實驗室主任。他1996年畢業於中國科技大學少年班,並於2001年在中國科技大學獲得博士學位,師從陳國良院士。目前,他的團隊在數據挖掘、社會計算和普適計算等領域展開創新性的研究。他在國際會議和學術期刊上發表了250餘篇學術論文,共被引用20000餘次,H指數63,1999年獲首屆微軟學者獎,多次在KDD、ICDM等頂級會議上獲最佳論文獎,並被邀請在HHME 2018, ASONAM 2017、Mobiquitous 2016、SocInfo 2015、W2GIS 2011等會議做大會主題報告。他是ACM、IEEE高級會員和計算機學會傑出會員,多次擔任頂級國際會議程序委員會委員和領域主席等職位。他是ACM Transactions on Social Computing, ACM Transactions on Intelligent Systems and Technology、Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)、Springer GeoInformatica、Elsevier Pervasive and Mobile Computing、CCF Transactions on Pervasive Computing and Interaction等雜誌編委。他參與創立了ACM SIGSPATIAL中國分會,並曾擔任ACM UbiComp 2011、PCC 2012、IEEE UIC 2015、以及SMP 2017等大會程序委員會共同主席。

    個人主頁:http://dsxt.ustc.edu.cn/zj_js.asp?zzid=1074

    https://www.microsoft.com/en-us/research/people/xingx/

  5. 張永鋒

    Rutgers大學計算機係助理教授。最近的研究集中在機器學習和數據挖掘、推薦和搜索係統、知識圖和計算經濟學的交叉上,包括1)解釋機器學習及其在決策支持係統中的應用--開發可解釋的機器學習理論和用於決策支持係統的算法,例如個性化推薦和搜索;2)基於神經網絡建模和自然語言處理的對話搜索、推薦和QA算法;3)網絡經濟學---應用和分析基於Web的應用中的經濟理論,如推薦、搜索和共享經濟。我的團隊也對"個性化X"感興趣,包括個性化推薦、搜索、教育、聊天機器人等。

    個人主頁:http://yongfeng.me/

  6. 何向南

    中國科學技術大學信息與技術學院、大數據學院教授。研究方向是信息檢索、數據挖掘和多媒體分析。共發表會議期刊論文六十餘篇,如SIGIR、WWW、KDD和MM,以及包括TKDE、TOIS和TMM在內的期刊。其推薦係統方麵的工作獲得了WWW 2018和ACM SIGIR 2016年度最佳論文獎的榮譽提名。此外還擔任過幾個頂級會議的高級PC成員,包括SIGIR、WWW、KDD和MM等,以及TKDE、TOIS和TMM等期刊的審稿人。

    個人主頁:http://staff.ustc.edu.cn/~hexn/

  7. 劉淇

    中國科學技術大學副教授、博導。研究方向為數據挖掘、機器學習、推薦係統、社交網絡分析.

    個人主頁:http://staff.ustc.edu.cn/~qiliuql/

  8. 李晨亮

    武漢大學副教授。武大珞珈青年學者,大數據分析與人工智能研究所(副所長)。研究方向為信息檢索、自然語言處理、統計學習、數據挖掘、社交媒體分析和挖掘。

    個人主頁:http://www.lichenliang.net/zh.html

  9. 趙鑫

北京大學博士,中國人民大學信息學院教師。研究領域為社交數據挖掘和自然語言處理領域,共發表CCF A/B、SCI論文40餘篇, Google Scholar引用1500餘次。博士期間的研究工作主要集中在社交媒體用戶話題興趣建模研究,同時獲得穀歌中國博士獎研金和微軟學者稱號。其中ECIR’11提出的Twitter-LDA成為短文本主題建模重要基準比較方法之一,單文引用次數近700次。目前主要關注與社會經濟緊密相關的商業大數據挖掘,研究用戶意圖檢測、用戶畫像以及推薦係統,將理論技術運用到實踐之中,承擔國家自然科學青年基金、北京市自然科學青年基金,入選第二屆CCF“青年人才托舉計劃”。擔任多個國際頂級期刊和學術會議評審、AIRS 2016出版主席、SMP 2017領域主席以及NLPCC 2017領域主席。 [http://playbigdata.com/batmanfly/]

  1. 劉奕群

    清華大學計算機科學與技術係副教授。主要研究興趣集中在網絡搜索引擎技術,尤其是基於用戶行為分析方法改進搜索引擎性能這一研究領域。麵對海量繁雜的網絡數據與千差萬別的用戶行為,傳統的信息檢索、機器學習、自然語言處理技術在搜索引擎係統中的應用麵臨著極大的挑戰。為應對這一挑戰,利用搜索引擎海量規模的用戶行為數據信息,發揮“用戶群體智慧”的作用是非常必要的。基於這一思路,其在國家自然科學基金重點項目、青年項目、教育部博士點基金項目與清華—搜狐搜索技術聯合實驗室的支持下開展了一係列相關研究。

    個人主頁:http://www.thuir.cn/group/~YQLiu/

  2. 唐建

    MILA-QuebecAI研究所和HEC蒙特利爾的助理教授。在此之前是密歇根大學和卡內基梅隆大學的博士後。2014-2016年間在微軟亞研工作。

    個人主頁:https://jian-tang.com/

  3. 穀文棟

    微信公眾號 resyschina , ResysChina發起人

  4. 洪亮劼

    Etsy數據科學主管,前雅虎研究院高級研發經理

  5. Yehuda Koren

    Netflix Prize冠軍隊成員,曾就職雅虎,現就職於穀歌,代表文獻:Matrix Factorization Techniques for Recommender Systems

  6. Jure Leskovec

    斯坦福大學計算機科學係副教授。研究重點是挖掘和建模大型的社會和信息網絡,它們的進化,以及信息的擴散和對它們的影響。調查的問題是由大規模數據、網絡和在線媒體推動的。

    個人主頁:https://cs.stanford.edu/~jure/

  7. Hao Ma

    個人主頁:https://www.haoma.io/

  8. Julian MaAuley

    加利福尼亞大學聖迭戈分校助理教授。研究方向為社交網絡、數據挖掘、推薦係統。

    個人主頁:https://cseweb.ucsd.edu/~jmcauley/

  9. Robin Burke

    科羅拉多大學波德分校教授。主要研究方向為個性化推薦係統。

    個人主頁:https://www.colorado.edu/cmci/people/college-leadership/robin-burke

  10. Bamshad Mobasher

    Bamshad Mobasher博士,芝加哥的計算和數字媒體學院網絡智能中心主任,計算機科學係教授和網絡智能中心主任。他也是德保羅大學數據挖掘和預測分析中心的共同創始人和總監。

    個人主頁:https://www.cdm.depaul.edu/Faculty-and-Staff/Pages/faculty-info.aspx?fid=653


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最近更新:2019-12-9

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隨著移動設備上存儲和計算能力的快速發展,在設備上部署模型以節省繁重的通信延遲和獲取實時特性變得至關重要和流行。雖然已經有很多研究致力於促進設備上的學習和推斷,但大多數研究都集中在處理響應延遲或隱私保護方麵。對設備和雲建模之間的協作進行建模並使雙方共同受益的工作很少。為了彌補這一差距,我們是研究設備-雲協作學習(DCCL)框架的首批嚐試之一。具體來說,我們在設備端提出了一種新穎的MetaPatch學習方法,以便在一個集中式的雲模型下有效地實現“成千上萬的人擁有成千上萬的模型”。然後,針對數十億更新的個性化設備模型,我們提出了一種“模型-超模型”的蒸餾算法,即MoMoDistill,來更新集中式雲模型。我們在一係列不同設置的數據集上進行了大量實驗,證明了這種協作在雲和設備上的有效性,特別是它在建模長尾用戶方麵的優越性。

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最新論文

Deep learning provides accurate collaborative filtering models to improve recommender system results. Deep matrix factorization and their related collaborative neural networks are the state-of-art in the field; nevertheless, both models lack the necessary stochasticity to create the robust, continuous, and structured latent spaces that variational autoencoders exhibit. On the other hand, data augmentation through variational autoencoder does not provide accurate results in the collaborative filtering field due to the high sparsity of recommender systems. Our proposed models apply the variational concept to inject stochasticity in the latent space of the deep architecture, introducing the variational technique in the neural collaborative filtering field. This method does not depend on the particular model used to generate the latent representation. In this way, this approach can be applied as a plugin to any current and future specific models. The proposed models have been tested using four representative open datasets, three different quality measures, and state-of-art baselines. The results show the superiority of the proposed approach in scenarios where the variational enrichment exceeds the injected noise effect. Additionally, a framework is provided to enable the reproducibility of the conducted experiments.

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