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Computer Science > Social and Information Networks

Title: Similar but Different: Exploiting Users' Congruity for Recommendation Systems

Abstract: 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.
Subjects: Social and Information Networks (cs.SI); Information Retrieval (cs.IR)
Cite as: arXiv:1803.04514 [cs.SI]
  (or arXiv:1803.04514v2 [cs.SI] for this version)

Submission history

From: Ghazaleh Beigi [view email]
[v1] Mon, 12 Mar 2018 20:31:47 GMT (226kb,D)
[v2] Fri, 16 Mar 2018 01:03:35 GMT (226kb,D)