Authors:
Silvia Rossi
;
Francesco Cervone
and
Francesco Barile
Affiliation:
Università degli Studi di Napoli Federico II, Italy
Keyword(s):
Group Recommendations, Weighted Utilities, Off-line Testing.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Group Decision Making
Abstract:
One of the major issue in designing group recommendation techniques relates to the difficulty of the evaluation process. Up-today, no freely available dataset exists that contains information about groups, like, for example, the group’s choices or social aspects that may characterize the group’s members. The objective of the paper is to analyze the possibility to make an evaluation of ranking-based groups recommendation techniques by using offline testing. Typically, the evaluation of group recommendations is computed, as in the classical
single user case, by comparing the predicted group’s ratings with respect to the single users’ ratings. Since the information contained in the datasets are mainly such user’s ratings, here, ratings are used to define different ranking metrics. Results suggest that such an attempt is hardly feasible. Performance seems not to be affected by the choice of ranking technique, except for some particular cases. This could be due to the averaging effect of
the evaluation with respect to the single users’ ratings, so a deeper analysis or specific dataset are necessary.
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