Learning Recommendations in Social Media Systems By Weighting Multiple Relations
We address the problem of item recommendation in social
media sharing systems. We adopt a multi-relational framework
capable to integrate different entity types available in
the social media system and relations between the entities.
We then model different recommendation tasks as weighted
random walks in the relational graph. The main contribution
of the paper is a novel method for learning the optimal contribution
of each relation to a given recommendation task, by
minimizing a loss function on the training dataset. We report
results of the relation weight learning for two common
tasks on the Flickr dataset, tag recommendation for images
and contact recommendation for users.
ECML PKDD 2011, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Athens, Greece 5-9 Sept. 2011.