'Collaborative filtering' diaporamas de présentation

Recommendation in Social Media

S OCIAL N ETWORKS. Modified from R . Zafarani , M. A. Abbasi , and H. Liu, Social Networks Mining: An Introduction , Cambridge University Press, 2014. Recommendation in Social Media. Difficulties of Decision Making. Which digital camera should I buy?

1.19k views • 85 slides

Big Data Analysis

Big Data Analysis. Chin- Chih Chang ??? changc@chu.edu.tw Computer Science and Information Engineering Chung Hua University 2014/03/24. Big Data Analysis. What is Big D ata? Why is Big Data important? How to do with these data?

725 views • 58 slides

??? ??? ??? ?? ?? ?? (Connectives based semantic information retrieval model)

??? ??? ??? ?? ?? ?? (Connectives based semantic information retrieval model). Contents. Motivation Limitations of keyword-based information retrieval Related Work Overview of Semantic Search Overview of Recommendation A Semantic Information Retrieval Model

703 views • 52 slides

Cross-domain Collaboration Recommendation

642 views • 46 slides

TrustWalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation

TrustWalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation. Mohsen Jamali & Martin Ester Simon Fraser University, Vancouver, Canada. Introduction TrustWalker Single Random Walk Recommendation Matrix Notation Properties of TrustWalker

546 views • 29 slides

Enhancing Discovery with Solr and Mahout

Enhancing Discovery with Solr and Mahout. Grant Ingersoll Chief Scientist Lucid Imagination. Evolution. Minding the Intersection. Topics. Background Apache Mahout Apache Solr and Lucene Recommendations with Mahout Collaborative Filtering Discovery with Solr and Mahout Discussion.

496 views • 28 slides

Recommender Systems

Recommender Systems. Presented By: Madiha Saleem Sunniya Rizvi. Collaborative Filtering. Collaborative filtering is a technique used by recommender systems to combine different users' opinions and tastes in order to achieve personalized recommendations. There are 3 approaches:

463 views • 27 slides

SINGULAR VALUE DECOMPOSITION (SVD)

SINGULAR VALUE DECOMPOSITION (SVD). BY: PARIN SHAH SIVANAGA PRASAD . WHAT IS SVD?. 1.) SVD as Factorization .

1.48k views • 25 slides

The Netflix Challenge Parallel Collaborative Filtering

The Netflix Challenge Parallel Collaborative Filtering. James Jolly Ben Murrell CS 387 Parallel Programming with MPI Dr. Fikret Ercal. What is Netflix?. subscription-based movie rental online frontend over 100,000 movies to pick from 8M subscribers 2007 net income: $67M.

608 views • 23 slides

CSE 6362.003 Intelligent Environments Paper Presentation Darin Brezeale April 16, 2003

CSE 6362.003 Intelligent Environments Paper Presentation Darin Brezeale April 16, 2003. Surfing the Digital Wave. Generalizing Personalized TV Listings using Collaborative, Case-Based Recommendation Barry Smyth, Paul Cotter Dept. of Computer Science University College Dublin.

345 views • 23 slides

Recommender Systems; Social Information Filtering

Recommender Systems; Social Information Filtering. Web Personalization & Recommender Systems. Dynamically serve customized content (pages, products, recommendations , etc.) to users based on their profiles, preferences, or expected interests

442 views • 21 slides

329 views • 21 slides

Intelligent and Adaptive Interfaces

370 views • 18 slides

User Selectable Interactive Recommendation System In Mobile Environment

User Selectable Interactive Recommendation System In Mobile Environment. Jung-Min Oh NamMee Moon Presented by: Dhanashree Lale. Mobile Environments used widely. Smart phones are popular Digital content is accessible more to people these days Variety of content has increased

286 views • 18 slides

COMP 4332 Tutorial 9 April 8 CHEN Zhao

Introducing Project 2/Assignment 3: KDDCUP 2012 (Track 1). COMP 4332 Tutorial 9 April 8 CHEN Zhao. KDDCup 2012 hosted by Tencent. Website by Kaggle : https://www.kddcup2012.org / Tencent , 5 th largest Internet Company in the world, by market cap(Google, Amazon, Ebay , Facebook).

299 views • 16 slides

Behavioral Analysis and Personalization

Behavioral Analysis and Personalization. By Srishti Gahlot (sg2856). What do you mean by online behavior? Why do we need to analyze online behavior and personalize it? How do we analyze this behavior? Comparison between Amazon and ebay Security issues Future work .

247 views • 15 slides

?????? ????? / Collaborative Filtering

?????? ????? / Collaborative Filtering. ?"? ??? ???????. ????? ?? ?????? ??? ?????. Supervised ?? Unsupervised. ????? Unsupervised ?? ???? ?? ? " Objective truth " (??? ??????????) ???? ??? ?????? ??? ??? ?????? ????? ????? ( Click-through ) ??? ????? Correlation

272 views • 14 slides

TENCompetence Kick-off Meeting Valkenburg, 9 th December 2005

208 views • 13 slides

Outline

Privacy-Enhanced Collaborative Filtering Privacy-Enhanced Personalization workshop July 25, 2005, Edinburgh, Scotland Shlomo Berkovsky 1 , Yaniv Eytani 1 , Tsvi Kuflik 2 , Francesco Ricci 3.

253 views • 13 slides

Lan Yi lan.yi@intel Senior Software Engineer Intel China Software Center 2013.07.16

Experience with HiBench From Micro-Benchmarks toward End-to-End Pipelines WBDB 2013 Workshop Presentation. Lan Yi lan.yi@intel.com Senior Software Engineer Intel China Software Center 2013.07.16. HiBench. Micro Benchmarks. Web Search. Different from GrixMix, SWIM? Micro Benchmark?

297 views • 9 slides