Generating predictive movie recommendations from trust in social networks
J. Golbeck. n Proceedings of the fourth international conference on trust management, (2006)
Abstract
Social networks are growing in number and size, with
hundreds of millions of user accounts among them. One added
benefit of these networks is that they allow users to encode more
information about their relationships than just stating who they
know. In this work, we are particularly interested in trust
relationships, and how they can be used in designing interfaces. In
this paper, we present FilmTrust, a website that uses trust in webbased
social networks to create predictive movie recommendations.
Using the FilmTrust system as a foundation, we show that these
recommendations are more accurate than other techniques when the
user's opinions about a film are divergent from the average. We
discuss this technique both as an application of social network
analysis, as well as how it suggests other analyses that can be
performed to help improve collaborative filtering algorithms of all
types.
%0 Conference Paper
%1 Gol06
%A Golbeck, Jennifer
%B n Proceedings of the fourth international conference on trust management
%D 2006
%K web2.0 recommendation trust tagging socialNetworks
%T Generating predictive movie recommendations from trust in social networks
%U http://trust.mindswap.org/papers/iTrust06.pdf
%X Social networks are growing in number and size, with
hundreds of millions of user accounts among them. One added
benefit of these networks is that they allow users to encode more
information about their relationships than just stating who they
know. In this work, we are particularly interested in trust
relationships, and how they can be used in designing interfaces. In
this paper, we present FilmTrust, a website that uses trust in webbased
social networks to create predictive movie recommendations.
Using the FilmTrust system as a foundation, we show that these
recommendations are more accurate than other techniques when the
user's opinions about a film are divergent from the average. We
discuss this technique both as an application of social network
analysis, as well as how it suggests other analyses that can be
performed to help improve collaborative filtering algorithms of all
types.
@inproceedings{Gol06,
abstract = {Social networks are growing in number and size, with
hundreds of millions of user accounts among them. One added
benefit of these networks is that they allow users to encode more
information about their relationships than just stating who they
know. In this work, we are particularly interested in trust
relationships, and how they can be used in designing interfaces. In
this paper, we present FilmTrust, a website that uses trust in webbased
social networks to create predictive movie recommendations.
Using the FilmTrust system as a foundation, we show that these
recommendations are more accurate than other techniques when the
user's opinions about a film are divergent from the average. We
discuss this technique both as an application of social network
analysis, as well as how it suggests other analyses that can be
performed to help improve collaborative filtering algorithms of all
types.},
added-at = {2006-11-07T20:50:20.000+0100},
author = {Golbeck, Jennifer},
biburl = {https://www.bibsonomy.org/bibtex/2a0166b40a2560895be8ace9588e33876/lysander07},
booktitle = {n Proceedings of the fourth international conference on trust management},
interhash = {6b96178a286eebfcf149a23b993bc1ea},
intrahash = {a0166b40a2560895be8ace9588e33876},
keywords = {web2.0 recommendation trust tagging socialNetworks},
timestamp = {2009-01-27T15:24:50.000+0100},
title = {Generating predictive movie recommendations from trust in social networks},
url = {http://trust.mindswap.org/papers/iTrust06.pdf},
year = 2006
}