Socially-Aware Diagnosis for Constraint-Based Recommendation

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband


Constraint-based group recommender systems support the identification of items that best match the individual preferences of all group members. In cases where the requirements of the group members are inconsistent with the underlying constraint set, the group needs to be supported such that an appropriate solution can be found. In this paper, we present a guided approach that determines socially-aware diagnoses based on different aggregation functions. We analyzed the prediction quality of different aggregation functions by using data collected in a user study. The results indicate that those diagnoses guided by the Least Misery aggregation function achieve a higher prediction quality compared to the Average Voting, Most Pleasure, and Majority Voting. Moreover, another major outcome of our work reveals that diagnoses based on aggregation functions outperform basic approaches such as Breadth First Search and Direct Diagnosis.
TitelUMAP '19: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization
Herausgeber (Verlag)Association of Computing Machinery
PublikationsstatusVeröffentlicht - 2019
Veranstaltung27th ACM Conference on User Modeling, Adaptation and Personalization - Larnaca, Zypern
Dauer: 9 Jun 201912 Jun 2019


Konferenz27th ACM Conference on User Modeling, Adaptation and Personalization
KurztitelUMAP 2019


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