Multi-granularity Recommendation Based on Ontology User Model
Jianxing Zheng, Bofeng Zhang, Guobing Zou · 2013
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.