<--- Back to Details
First PageDocument Content
Recommender systems / Collaborative filtering / MovieLens / LibraryThing / GroupLens Research / Cold start
Date: 2013-07-02 23:38:23
Recommender systems
Collaborative filtering
MovieLens
LibraryThing
GroupLens Research
Cold start

Proceedings Template - WORD

Add to Reading List

Source URL: robinnaughton.com

Download Document from Source Website

File Size: 60,17 KB

Share Document on Facebook

Similar Documents

Recommender systems / Collective intelligence / Information science / Information / Computing / Social information processing / Collaboration / Collaborative filtering / Cold start / MovieLens / Educational technology / Item-item collaborative filtering

A Hybrid Peer Recommender System for an Online Community of Teachers Cristian Miranda U. Austral de Chile Casilla 567, Valdivia

DocID: 1qM2s - View Document

Recommender systems / Collaborative filtering / MovieLens / GroupLens Research / Cold start

Building a Lifestyle Recommender System Supiya Ujjin and Peter J. Bentley University College London Department of Computer Science Gower Street, London WC1E 6BT

DocID: 1pjFt - View Document

Data mining / Archival science / Evidence law / Museology / Provenance / Scientific method / Cluster analysis / MovieLens

Approximated Summarization of Data Provenance Eleanor Ainy Tel Aviv University Pierre Bourhis

DocID: 1pcaE - View Document

MyersBriggs Type Indicator / MovieLens / Personality type / Personality test / Recommender system / Cognitive style / Isabel Briggs Myers / MBTI Step II

Does an Individual’s Myers-Briggs Type Indicator Preference Influence Task-Oriented Technology Use? Pamela Ludford and Loren Terveen University of Minnesota, Department of Computer Science and EngineeringEE/CS B

DocID: 1oROf - View Document

Recommender systems / Humancomputer interaction / Collective intelligence / MovieLens / Privacy / Collaborative filtering / Information privacy / Internet privacy / Personalization / Differential privacy / Facebook like button / GroupLens Research

A User-aware Clicking Platform for Recommenders Rachid Guerraoui Mahsa Taziki EPFL

DocID: 1oNtz - View Document