Collaborative filtering attempts to find items

of interest for a user by utilizing the preferences

of other users. In this paper we describe

an approach to filtering that explicitly

uses social relationships, such as friendship,

to find items of interest to a user. Modeling

user-item relations as a bipartite graph

we augment it with user-user (social) links

and propose an absorbing random walk that

induces a set of stationary distributions, one

per user, over all items. These distributions

can be interpreted as personalized rankings

for each user. We exploit sparsity of both

user-item and user-user relationships to improve

the efficiency of our algorithm.

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In: North East Student Colloquium on Artificial Intelligence

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