Information Push System Based On Intranet

With the rapid development of Internet, the information collection, release and related retrieval transaction based on the Internet has brought new concepts for our world. Therefore, the transaction of Internet/Intranet has developed into the focus of people gradually. Under this background, the technology of push arises at this moment.The essence of push is to let the information find user. The advantage of push lies in its initiative.Through using the technology, the system can push information to users. It's disadvantage lies in the inaccuracy of information. Because simple screening mechanism has replaced the artificial choice, there must exist certain differences between the webpages obtained and the real demand of user. The push technology hasn't made enormous success in Internet, the reasons lie in many aspects. To ISP, it is too complicated because of the diversity of people on one hand; on the other hand, owing to the limit of bandwidth. Considering the similarity among users in an unit, it is possible to apply push technology in intranetoSo, we do some research on the Push System based on Intranet(PSI). Our work: firstly, the system gained every group's interest based on examples and build up corresponding model; secondly, according to every group's query keywords, we get a set of documents using existing search engine (google, baidu). We use vector space model to denote the group's interest and the returning documents into vector {(k1,V2),(k2,v2)...(kn,vn)}, then we can calculate the similarity between them using the formula of cosine. The biggest former N pages will be pushed to group. Finally, in the feedback unit, we use arithmetical mean and D-S evidence theory to cope with the feedback of users in every group. The aim is to synthesize the users' interest and obtain a value. Thus we can update the initial profile bitterly and can improve the precision of push.Further work: 1. trying to calculate the weight of keyword in users' interest model and documents; 2. analyzing which index is better whether the number of documents push or the value of similarity; 3. perfecting the push system based on Intranet.