An Architecture Framework for Collective Intelligence Systems Juergen Musil⇤ , Angelika Musil⇤ , Danny Weyns† and Stefan Biffl⇤ ⇤ CDL-Flex, Institute of Software Technology and Interactive Systems,

Source URL: homepage.lnu.se

C OV ER F E AT U RE MATRIX FACTORIZATION TECHNIQUES FOR RECOMMENDER

Source URL: datajobs.com

• Recommender systems
• Collective intelligence
• Information systems
• Collaboration
• Collaborative filtering
• Cold start
• Netflix Prize
• Personalization
• Singular value decomposition
• Item-item collaborative filtering
• Robust collaborative filtering

Style in the Long Tail: Discovering Unique Interests with Latent Variable Models in Large Scale Social E-commerce Diane Hu Rob Hall

Source URL: mimno.infosci.cornell.edu

• Recommender systems
• Social information processing
• Collective intelligence
• Social networking services
• Humancomputer interaction
• Collaborative filtering
• Etsy
• Cold start
• Latent Dirichlet allocation
• Facebook
• GroupLens Research
• Personalization

Taxonomy Manager TM Unlock the power of your information. At Enthink we believe there are solutions to make your

Source URL: www.enthink.com

• Information science
• Information retrieval
• Information
• Knowledge representation
• Collective intelligence
• Technical communication
• Computer jargon
• Tag
• Index term
• Search engine optimization
• Folksonomy

Digital Library Curriculum Development Module 7-c: Recommender Systems (Draft, Module title: Recommender Systems 2. Module Scope: This module addresses the concepts underlying Recommender Systems, along with

Source URL: curric.dlib.vt.edu

• Information science
• Information
• Recommender systems
• Computing
• Collective intelligence
• Information systems
• Humancomputer interaction
• Social information processing
• Collaborative filtering
• Cold start
• Preference elicitation
• Personalization

2013 IEEE/WIC/ACM International Conferences on Web Intelligence (WI) and Intelligent Agent Technology (IAT) MeDetect: A LOD-based System for Collective Entity Annotation in Biomedicine Li Tian1, Weinan Zhang2, Antonis B

Source URL: www0.cs.ucl.ac.uk

Digital Library 2.0 for Educational Resources Monika Akbar1 , Weiguo Fan1 , Clifford A. Shaffer1 , Yinlin Chen1 , Lillian Cassel2 , Lois Delcambre3 , Daniel D. Garcia4 , Gregory W. Hislop5 , Frank Shipman6 , Richard Fu

Source URL: www.cs.utep.edu

• Humancomputer interaction
• Information Age
• Social information processing
• Collective intelligence
• Web 2.0
• Library 2.0
• Personalization
• Social networking service
• World Wide Web
• Usability
• Tag
• National Science Digital Library

Religious Politicians and Creative Photographers: Automatic User Categorization in Twitter Claudia Wagner∗ , Sitaram Asur† and Joshua Hailpern† ∗ Institute of Information and Communication Technologies, JOANNEUM

Source URL: claudiawagner.info

• Software
• Digital media
• Computing
• Social networking services
• Collective intelligence
• Social media
• Universal Windows Platform apps
• Web 2.0
• Recommender system
• Twitter
• Facebook
• Tag

SANJAY KAIRAM RESEARCH STATEMENT My research in human-computer interaction addresses implications of the large-scale digital traces left behind by

Source URL: www.sanjaykairam.com

• Digital media
• Technology
• New media
• Structure
• Social information processing
• Social networks
• Network theory
• Social networking service
• Social network
• Social media
• Collective intelligence
• Internet

Explaining Collective Impact According to the Stanford Social Review, Collective Impact is the commitment of a group of important actors from different sectors to a common agenda for solving a specific social problem. Co

Source URL: www.ppunitedway.org

• Collective impact
• Philanthropy
• Social responsibility
• Collective intelligence