Aggregating user preferences in group recommender systems: A crowdsourcing approach

İSMAİLOĞLU F.

DECISION SUPPORT SYSTEMS, cilt.152, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 152
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.dss.2021.113663
  • Dergi Adı: DECISION SUPPORT SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, International Bibliography of Social Sciences, PASCAL, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Psycinfo, Civil Engineering Abstracts
  • Anahtar Kelimeler: Recommender systems, Crowdsourcing, Ordinal classification, Bayesian learning, Expectation maximization
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

We present that group recommendations are similar to crowdsourcing, where the responses of different crowd workers are aggregated in the absence of ground truth. With this in mind, we mimic the use of the EM algorithm as in crowdsourcing to aggregate the preferences of group members to estimate group ratings and the expertise levels the group members. Moreover, for the first time in the literature, we cast the problem of estimating group rating as an ordinal classification problem relying on the natural ordering between the ratings, which allows us to define the expertise levels of the members in terms of sensitivity and specificity. In fact, we impose priors on the sensitivity and the specificity scores corresponding to the members, taking a Bayesian approach. We validate the effectiveness of the proposed aggregation method using the CAMRa2011 dataset, which consists of small and established groups, and the MovieLens dataset, which consists of large and random groups.