Representation of cases in group recommender systems by combining users' perceived feature importance weights
In this paper we describe a case based approach to group recommendation process in which more than one person is involved in the recommendation process. The main problem group recommendation needs to solve is how to adapt to the group as a whole based on item features describing individual user preferences. Our approach takes into account that the distribution of individually perceived feature importance weights variate among members of the group. The two methods to case representation are presented: case representation by combining individually perceived feature importance weights and case representation by combining averaged perceived feature importance weights. In order to compare these two methods to case representation, the two metrics widely used in information retrieval (recall and precision) are used.