HYBRID PROFILING FOR HYBRID MULTICRITERIA RECOMMENDATION BASED ON IMPLICIT MULTICRITERIA INFORMATION

摘要

Mullicrileria recommender systems typically gather the user preferences by asking a user to rale different aspects of an item on a sliding scale explicitly. However, this approach could possibly cause intrusiveness and conflict on user preferences. For example, an individual's preference on each aspect of an item may conflict with an overall preference. To overcome such limitations, we proposed the hybrid profiling framework to generate a set of useful implicit data-set to support mullicrileria reccmmender systems. We also proposed two hybrid mullicrileria recommendation approaches, namely the user-attribute-based (UAH) and the user-item matching (UIM) to improx'e recommendation accuracy. Finally, we conducted experiments to confirm the efficiency of the proposed approaches. The experiments show that the profiling framework and two hybrid recommendation approaches can alleviate the problem in an intrusive manner and decrease the degree of preference conflict without decreasing the accuracy of the recommendation. They also show that our proposed hybrid mullicrileria recommendation approaches can significantly outperform both the traditional collaborative filtering and the simple mullicrileria filtering approaches.