Socially-Aware Diagnosis for Constraint-Based Recommendation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish
Title of host publicationUMAP '19: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization
PublisherAssociation of Computing Machinery
Pages121-129
Number of pages9
DOIs
Publication statusPublished - 2019
Event27th ACM Conference on User Modeling, Adaptation and Personalization - Larnaca, Cyprus
Duration: 9 Jun 201912 Jun 2019

Conference

Conference27th ACM Conference on User Modeling, Adaptation and Personalization
Abbreviated titleUMAP 2019
CountryCyprus
CityLarnaca
Period9/06/1912/06/19

Keywords

  • socially aware diagnosis
  • minimal diagnosis
  • configuration
  • group decision making
  • social choice theory
  • decision heuristics
  • Group Recommender Systems

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