Multi-granularity Recommendation Based on Ontology User Model

The traditional personalized recommendation system supplies the target user with top k items in fixed interest subject. However, the recommended items cover the coarse subject level and the accuracy performance is poor. Taking into account ontology structure of subject, user´s actual interests can distribute in multiple sub-subject structures. In this paper, multi-granularity recommendation mechanism relying on multi-granularity similarity is proposed to fit user´s actual detail demands. Specially, a personalized ontology user model is learned to represent user´s multi-granularity interests. According to ontology structure, the multi-granularity similarity method is implemented by combing content closeness and semantic closeness between user models at different grained subjects. Lastly, recommendation method distributed in multi-granularity subjects is achieved to compare against traditional single subject´s recommendation for their performances. The experimental results show that the proposed mechanism is more successful.