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.

Original languageEnglish (US)
Title of host publicationSocial, Cultural, and Behavioral Modeling - 11th International Conference, SBP-BRiMS 2018, Proceedings
EditorsHalil Bisgin, Robert Thomson, Ayaz Hyder, Christopher Dancy
PublisherSpringer Verlag
Pages129-140
Number of pages12
ISBN (Print)9783319933719
DOIs
StatePublished - Jan 1 2018
Event11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018 - Washington, United States
Duration: Jul 10 2018Jul 13 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10899 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018
CountryUnited States
CityWashington
Period7/10/187/13/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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