'Recommender system' presentation slideshows

Database Support for Recommender Systems

Database Support for Recommender Systems. Mohamed F. Mokbel Department of Computer Science and Engineering, University of Minnesota www.cs.umn.edu/~mokbel mokbel@cs.umn.edu. Talk Outline. Background on Recommender Systems DBMS for Recommender Systems

707 views • 54 slides

C onnecting the Dots to Improve Cyberlearning

C onnecting the Dots to Improve Cyberlearning. Leyla Zhuhadar , Ph.D . Research Scientist, Office of Distance Learning, Western Kentucky University, USA. Adjunct Assistant Prof. CECS Dept., University of Louisville , USA. Prepared for NSF Cyberlearning

575 views • 51 slides

CLIRS: Customer Loyalty Improvement Recommender System .

CLIRS: Customer Loyalty Improvement Recommender System. Built for the Daniel Group. (2013-2018). presented by Zbigniew W. Ras. Research sponsored by. Project Team. KDD Lab Members. Kasia Tarnowska (San Jose State University). Pauline Brunet (Paris Tech., France ). Jieyan Kuang

687 views • 46 slides

Presented by: Muhammad Nuruddin , student ID: 2961230, email address: nuruddin@L3S.de,

Presentation on the papers.

442 views • 35 slides

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???????????. Related papers ? Modeling Evolutionary Behaviors for Community-based Dynamic Recommendation --SIAM 2. Learning Preferences of New Users in Recommender System : An Information Theoretic Approach --SIGKDD. ??????? 2011:12:10. Will user U like item X?.

507 views • 31 slides

Collaborative Filtering & Recommender Systems

Junaid Alam Arish Joyo Imran Allawala Anum Mazhar. Collaborative Filtering & Recommender Systems. How Often Do We See The Following. Youtube.com. Download.cnet.com. Amazon.com. The Definition.

517 views • 28 slides

Expanding User Features with Social Relationships in Social Recommender Systems

Expanding User Features with Social Relationships in Social Recommender Systems. Chengjie Sun,Lei Lin, Yuan Chen, Bingquan Liu Harbin Institute of Technology School of Computer Science and Technology. Outlines. Background and challenges Related works Methodology description

369 views • 28 slides

Attacks on collaborative recommender systems

Attacks on collaborative recommender systems. Agenda. Introduction Charactarization of Attacks Attack models Effectivness analysis Countermeasures Privacy aspects Discussion. Introduction / Background. (Monetary) value of being in recommendation lists

548 views • 27 slides

Hybrid-?-greedy for Mobile Context-Aware Recommender System

363 views • 24 slides

Prof. Dr. Torky Sultan Faculty of Computers and Information, Helwan University

Recommender System Role in e-Learning Environment. Prof. Dr. Torky Sultan Faculty of Computers and Information, Helwan University e-Mail: tsultan@consultant.com Dr. Mona Nasr Faculty of Computers and Information, Helwan University e-Mail: m.nasr@helwan.edu.eg Mr. Mohamed Saied

366 views • 21 slides

Personalized Recommender Systems in e-Commerce and m-Commerce: A Comparative Study

Personalized Recommender Systems in e-Commerce and m-Commerce: A Comparative Study. Azene Zenebe, Ant Ozok and Anthony F. Norcio Department of Information Systems University of Maryland Baltimore County (UMBC) Baltimore, MD 21250 USA. Outline. Introduction m-commerce verse e-commerce

497 views • 21 slides

Recommender Systems

Sumbul Jahan. Recommender Systems. This world is an over-crowded place. They all want to get our attention. But we need a few of them!. Something which is popular. Something which is of our interest. Something which is liked by people of our community. What we are looking for?.

283 views • 19 slides

Peer Interaction Effectively, yet Infrequently, Enables Programmers to Discover New Tools

256 views • 16 slides

Utilizing Marginal Net Utility for Recommendation in E-commerce

322 views • 13 slides

Wolfgang Woerndl , Henrik Muehe, Stefan Rothlehner, Korbinian Moegele

Context-Aware Recommendations in Decentralized, Item-Based Collaborative Filtering on Mobile Devices. Wolfgang Woerndl , Henrik Muehe, Stefan Rothlehner, Korbinian Moegele Technische Universitaet Muenchen Munich, Germany. Motivation. Recommender systems

272 views • 12 slides

Cutting Through the Crud

Cutting Through the Crud. How BCI and Recommender Systems Might Improve the Downhill Skiing Experience. Ski Equipment Selection. Brands : Atomic, Dynastar, Fischer, K2, Rossignol, Volkl, Salomon, Tecnica, Head, Tyrolia, among many other large and boutique brands.

192 views • 7 slides

Collaborative Filtering

Collaborative Filtering. Presented by; Ghulam Mujtaba MS CS, IBA, Karachi.

243 views • 7 slides

Learning Environment Enhancement Using Recommender System technique .

Learning Environment Enhancement Using Recommender System technique . student : Hamdan abu taiema , Ali abo Ouda , Supervisor :Ayman S.A. Ghabayen ???? ?????? ???????????? . Outline . Introduction . Adaptive systems/ Recommendation Adaptive Software Systems Major Issues

189 views • 6 slides

Lanbo Zhang

170 views • 1 slides

Weekly Project Dashboard: Project Name: QE2

Weekly Project Dashboard: Project Name: QE2 Name: Qinyun Zhu Date: 7/12/2012. R. Key Accomplishments for this Reporting Period Read papers and materials about Big Data and some techniques about asynchronous data streaming.

165 views • 1 slides