Investigation of egocentric social structures for diversity-enhancing followee recommendations

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

Abstrakti

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.

AlkuperäiskieliEnglanti
OtsikkoACM UMAP 2019 Adjunct - Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization
KustantajaACM
Sivut257-261
Sivumäärä5
ISBN (elektroninen)978-1-4503-6711-0
DOI - pysyväislinkit
TilaJulkaistu - 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaACM International Conference on User Modeling, Adaptation and Personalization -
Kesto: 1 tammik. 2019 → …

Conference

ConferenceACM International Conference on User Modeling, Adaptation and Personalization
Ajanjakso1/01/19 → …

Tutkimusalat

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

Julkaisufoorumi-taso

  • Jufo-taso 1

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