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TUTCRIS

Investigation of egocentric social structures for diversity-enhancing followee recommendations

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoACM UMAP 2019 Adjunct - Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization
KustantajaACM
Sivut257-261
Sivumäärä5
ISBN (elektroninen)9781450367110
DOI - pysyväislinkit
TilaJulkaistu - 6 kesäkuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
Tapahtuma ACM International Conference on User Modeling, Adaptation and Personalization - Larnaca, Kypros
Kesto: 9 kesäkuuta 201912 kesäkuuta 2019

Conference

Conference ACM International Conference on User Modeling, Adaptation and Personalization
MaaKypros
KaupunkiLarnaca
Ajanjakso9/06/1912/06/19

Tiivistelmä

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.

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