Recent Advances in Personal Recommender Systems
نویسندگان
چکیده
In the past years we have witnessed an explosive growth of the data and information on the World Wide Web, which makes it difficult for normal users to find the information that they are interested in. On the other hand, the majority of the data and resources are very unpopular, which can be considered as “hidden information”, and are very difficult to find. By building a bridge between the users and the objects and constructing their similarities, the Personal Recommender System (PRS) can recommend the objects that the users are potentially interested in. PRS plays an important role in not only social and economic life but also scientific analysis. The interdisciplinary PRS attracts attention from the communities of information science, computational mathematics, statistical physics, management science, and consumer behaviors, etc. In fact, PRS is one of the most efficient tools to solve the information overload problem. According to the recommendation algorithms, we introduce four typical systems, including the collaborating filtering system, the content-based system, the structure-based system, and the hybrid system. In addition, some improved algorithms are proposed to overcome the limitations of traditional systems. This review article may shed some light on the study of PRS from different backgrounds.
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