Exploiting relational tag expansion for dynamic user profile in a tag-aware ranking recommender system

A tag-aware recommender system (TRS) presents the challenge of tag sparsity in a user profile. Previous work focuses on expanding similar tags and does not link with corresponding resources, therefore leading to static profile recommendation. In this article, we have proposed new social expansion model (STEM) generate dynamic improve recommendation performance. Instead simply including most relevant tags, completeness through by exploiting their relations includes sufficient set alleviate problem. The novel STEM-based TRS contains three operations: (1) Tag cloud generation discovers potentially an application domain; (2) finds upon original tags; (3) User refactoring builds determines weights extended We analysed STEM property terms accuracy demonstrated its performance extensive experiments over multiple datasets. analysis experimental results showed that technique was able correctly find solving At point, has consistently outperformed state-of-art methods these experiments.