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Learning collaborative filtering and its applications to people to people recommendation in social networks
Citation
Cai, X and Bain, M and Krzywicki, A and Wobcke, W and Kim, YS and Compton, P and Mahidadia, A, Learning collaborative filtering and its applications to people to people recommendation in social networks, Proceedings of the 10th IEEE International Conference on Data Mining, 14-17 December 2010, Sydney, Australia, pp. 743-748. ISBN 978-1-4244-9131-5 (2010) [Refereed Conference Paper]
Copyright Statement
Copyright 2010 IEEE
DOI: doi:10.1109/ICDM.2010.159
Abstract
Predicting people who other people may like has
recently become an important task in many online social
networks. Traditional collaborative filtering (CF) approaches
are popular in recommender systems to effectively predict user
preferences for items. One major problem in CF is computing
similarity between users or items. Traditional CF methods often
use heuristic methods to combine the ratings given to an item
by similar users, which may not reflect the characteristics of
the active user and can give unsatisfactory performance. In
contrast to heuristic approaches we have developed CollabNet,
a novel algorithm that uses gradient descent to learn the
relative contributions of similar users or items to the ranking of
recommendations produced by a recommender system, using
weights to represent the contributions of similar users for each
active user. We have applied CollabNet to the challenging problem
of people to people recommendation in social networks,
where people have a dual role as both "users" and "items", e.g.,
both initiating and receiving communications, to recommend
other users to a given user, based on user similarity in terms of
both taste (whom they like) and attractiveness (who likes them).
Evaluation of CollabNet recommendations on datasets from a
commercial online social network shows improved performance
over standard CF.
Item Details
Item Type: | Refereed Conference Paper |
---|---|
Keywords: | recommender systems, social network analysis |
Research Division: | Information and Computing Sciences |
Research Group: | Artificial intelligence |
Research Field: | Artificial intelligence not elsewhere classified |
Objective Division: | Information and Communication Services |
Objective Group: | Information systems, technologies and services |
Objective Field: | Application software packages |
UTAS Author: | Kim, YS (Dr Yang Kim) |
ID Code: | 94647 |
Year Published: | 2010 |
Deposited By: | Information and Communication Technology |
Deposited On: | 2014-09-15 |
Last Modified: | 2015-02-13 |
Downloads: | 0 |
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