A hybrid recommendation approach for hierarchical items

Abstract

Recommender systems aim to recommend items that are likely to be of interest to the user. In many business situations, complex items are described by hierarchical tree structures, which contain rich semantic information. To recommend hierarchical items accurately, the semantic information of the hierarchical tree structures must be considered comprehensively. In this study, a new hybrid recommendation approach for complex hierarchical tree structured items is proposed. In this approach, a comprehensive semantic similarity measure model for hierarchical tree structured items is developed. It is integrated with the traditional item-based collaborative filtering approach to generate recommendations.