The Blog-article Recommendation System(BARS)
Li-Hua Li, Fu-Ming Lee, Shang-Chi Chan · 2008
blog sites is up rising to 72 millions and the popularity of blogers has drawn many attention. This phenomenon has turned many web users into bloggers. The vast amount of blog information also brings the phenomenon of information overloading which is not handled by the blog function yet. In addition, the personalized recommendation service, which should be provided, is also not incorporated in the blog function now. To better service the bloggers and to overcome the above problems, this research proposes a Blog Article Recommendation System (BARS) which provides personalized article recommendation based on blogger’s interests. This research adapts the ontology technique in BARS to construct a personal preference tree for understanding blogger’s interests. The ART (Adaptive Resonance Theory) network is also utilized to cluster the group with similar interests. In order to find the similar preference between target blogger and the corresponding neighbors, we apply the Collaborative Filtering (CF) technique to generate the recommendation. The “cold-start ” problem, i.e. lacking of blogger’s usage data at the very beginning, is handled by combining ontology and Content-Based (CB) filtering method to infer the potential preference in BARS. The purpose of this research is to achieve the followings. (1) To solve the problem of information overloading. (2) To propose the cold-start problem when making the recommendation. (3) To build the BARS for personalized blog-article recommendation.