Similar but Different: Exploiting Users' Congruity for Recommendation Systems

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.

• Publication year

  2018

• Venue

  International Conference on Social, Cultural, and Behavioral Modeling

• Publication date

  2018-03-12

• Fields of study

  Computer Science, Psychology

• Identifiers
  DOI 10.1007/978-3-319-93372-6_15arXiv 1803.04514
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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