Study On Recommendation System Of Technological Literatures Using Case-Based Reasoning(CRB)

In the past few years, the Internet develops very fast, and it is easier and more direct to get various information. Because Internet is an open, dynamic and non-homogeneous network, lacking unified architecture and management, it is virtually impossible to separate the documents that interest a given user from myriad of other documents. Now, how to find information valuable is a time consuming process. There are many search engines, such as Yahoo and WebCrawter, which can help people to search the web based on keywords offered by users and certain match strategies. They can find relevant URLs in the index database and send back to users. Because of the fuzzy character in languages, the same keywords often appear in different contexts, and it is difficult for users to select suitable keywords to express what they want accurately. The result of search engines often contains so much non-relevant information that people have to spend much time and effort to navigate but may not find any personalized information.Personalized Recommendation technique, which is developed to attack this problem. According to different users' various tastes, it holds the promise to serve their customers to find resources on WWW voluntarily, by collecting and analyzing the users' interests and behaviors. For the moment, there exist many Personalized Recommendation models, being classified in two classes, in substance: Content-based system and Collaborative Filtering system. The latter has the vantage of finding new resources which may interest users, but do have two scabrous problems: one is the sparsity problem which the system can not find similar users due to system resources' lacking of enough ratios, at the initial stage of system; The other is the expansibility that the system's performance will become more worse because of its users and resources become more and more.By using Case-Based Reasoning(CBR), this article presents an approach to address the sparsity problem ; And uses Euclid distance to replace cosine representation as the computational method of users' similarity in order to attack the efficiency problem; At the same time, we also address a simple, workable tactic in the light of updating user's profile.