  The pervasive use of social media provides massive data about individuals'
online social activities and their social relations. The building block of most
existing recommendation systems is the similarity between users with social
relations, i.e., friends. While friendship ensures some homophily, the
similarity of a user with her friends can vary as the number of friends
increases. Research from sociology suggests that friends are more similar than
strangers, but friends can have different interests. Exogenous information such
as comments and ratings may help discern different degrees of agreement (i.e.,
congruity) among similar users. In this paper, we investigate if users'
congruity can be incorporated into recommendation systems to improve it's
performance. Experimental results demonstrate the effectiveness of embedding
congruity related information into recommendation systems.
