Aggregating user preferences in group recommender systems: A crowdsourcing approach
| Autor: | Firat Ismailoglu |
|---|---|
| Přispěvatelé: | Mühendislik Fakültesi |
| Rok vydání: | 2022 |
| Předmět: |
Information Systems and Management
business.industry Group (mathematics) Computer science Bayesian probability Aggregate (data warehouse) Recommender Systems Recommender system Crowdsourcing Machine learning computer.software_genre MovieLens Management Information Systems Random group Arts and Humanities (miscellaneous) Group Recommendation Prior probability Developmental and Educational Psychology Artificial intelligence business computer Information Systems |
| Zdroj: | Decision Support Systems. 152:113663 |
| ISSN: | 0167-9236 |
| DOI: | 10.1016/j.dss.2021.113663 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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