An enhanced aggregation method considering deviations for a group recommendation

Young Duk Seo, Young Gab Kim, Euijong Lee, Kwang Soo Seol, Doo Kwon Baik

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)


The goal of a group recommendation involves providing appropriate information for all members in a group. Most extant studies use aggregation methods to determine group preferences. An aggregation method is an approach that aggregates individual preferences of group members to recommend items to a group. Previous studies on aggregation methods only consider high averages, counts, and rankings to provide recommendations. However, the most important component of a group recommendation involves ensuring that majority of the members in a group are satisfied with the recommended results. Therefore, it is necessary to consider the deviation as an important element in aggregation methods. The present study involves proposing an upward leveling (UL) aggregation method that considers deviations for group recommendations. The UL recommends items with low deviations and high averages in conjunction with frequency of positive rating counts for group members. Furthermore, the effectiveness of the UL is validated to perform a comparative evaluation with existing aggregation methods by using the normalized discounted cumulative gain (NDCG) and diversity. The results indicate that the UL outperforms all the baselines and that the deviation plays an important role in the aggregation method.

Original languageEnglish
Pages (from-to)299-312
Number of pages14
JournalExpert Systems With Applications
Publication statusPublished - 2018 Mar 1

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( 2016R1D1A1B03936375 ).

Publisher Copyright:
© 2017 Elsevier Ltd


  • Aggregation method
  • Approval voting
  • Average
  • Deviation
  • Group recommendation
  • Upward leveling

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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