Experimental Analysis of Multiattribute Utility Collaborative Filtering on a Synthetic Data Set

摘要

Recommender systems have already been engaging multiple criteria for the production of recommendations. Such systems, referred to as multi-criteria recommenders, early demonstrated the potential of applying Multi-Criteria Decision Making (MCDM) methods to facilitate recommendation in numerous application domains. On the other hand, systematic implementation and testing of multi-criteria recommender systems in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined the importance of carrying out careful testing and parameterization of a recommender system, before it is actually deployed in a real setting. In this paper, the experimental analysis of several design options for three proposed multi-attribute utility collaborative filtering algorithms is presented. The data set used is synthetic, with multi-criteria evaluations that have been created using an appropriate simulation environment. This synthetic data set tries to mimic the evaluations that are expected to be collected from users in a particular application setting. The aim of the experiment is to demonstrate how a synthetic data set may be created and used to facilitate the study and selection of an appropriate recommendation algorithm, in the case that multi-criteria evaluations from real users are not available.