|
Abstract:
|
Recommender Systems are used to select online information relevant
to a given user . Traditional (memory based ) recommenders explore the user -item rating matrix and make recommendations based on users who have rated similarly or items that have been rated similarly . With the growing popularity of social networks , recommender systems can benefit from combining history of user preferences with information from the social /trust network of users . This thesis explores two techniques of combining user -item rating history with trust network information to make better user -item rating predictions . The first
approach (SCOAL [5] ) simultaneously co -clusters and learns separate models
for each co -cluster . The co -clustering is based on the user features as well as
the rating history . This captures the intuition that certain groups of users have similar preferences for certain groups of items . The grouping of certain users is affected by the similarity in the rating behavior and the trust network .
The second graph -based label propagation approach (MAD [27] ) works in a transductive setting and propagates ratings of user -item pairs directly on the
user social graph . We evaluate both approaches on two large public data -sets from Epinions .com and Flixster .com .
The thesis is amongst the first to explore the role of distrust in rating prediction . Since distrust is not as transitive as trust i .e . an enemy's enemy need not be an enemy or a friend , distrust can't directly replace trust in trust
propagation approaches . By using a low dimensional representation of the original trust network in SCOAL , we use distrust as it is and don't propagate it . Using SCOAL , we can pin -point the groups of users and the groups of
items that have the same preference model . Both SCOAL and MAD are able to seamlessly integrate side information such as item -subject and item -author
information into the trust based rating prediction model . |