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Investigation of egocentric social structures for diversity-enhancing followee recommendations

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationACM UMAP 2019 Adjunct - Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization
PublisherACM
Pages257-261
Number of pages5
ISBN (Electronic)9781450367110
DOIs
Publication statusPublished - 6 Jun 2019
Publication typeA4 Article in a conference publication
Event ACM International Conference on User Modeling, Adaptation and Personalization - Larnaca, Cyprus
Duration: 9 Jun 201912 Jun 2019

Conference

Conference ACM International Conference on User Modeling, Adaptation and Personalization
CountryCyprus
CityLarnaca
Period9/06/1912/06/19

Abstract

The increasing amount of data in social media enables new advanced user modeling approaches. This paper focuses on user profiling for diversity-enhancing recommender systems for finding new followees on Twitter. By combining social network analysis with Latent Dirichlet Allocation based content analysis, we defined three egocentric structural positions on the network extracted from Twitter data: Mentions of Mentions, Community Cluster, Dormant Ties (and the rest as a baseline condition). In addition to describing the data analysis procedure, we report preliminary empirical findings on a user-centered evaluation study of recommendations based on the proposed matching strategy and the presented structural positions. The investigation of the possible overlaps of the groups and the participants' evaluations of perceived relevance of the recommendation imply that the three positions are sufficiently mutually exclusive and thus could serve as new diversity-enhancing mechanisms in various people recommender systems.

ASJC Scopus subject areas

Keywords

  • Hybrid recommendation system, People recommender system, Social network analysis, Social recommender system, Twitter analytics, User modeling for social matching

Publication forum classification

Field of science, Statistics Finland