Personalized Web Information Recommendation Algorithm Based on Support Vector Machine

With the explosion of Web information, how to immediately and exactly find the needed information for each user has become a tough problem. To meet the personalized needs of users in information service, a new personalized recommendation algorithm based on support vector machine was proposed in the paper. First, user profile was organized hierarchically into field information and atomic information needs, considering similar information needs in the group users. Support vector machine was adopted for collaborative recommendation in classification mode, and then vector space model was used for content-based recommendation according to atomic information needs. The algorithm had overcome the demerit of using collaborative or content-based recommendation solely, which improved the precision and recall in a large degree. It also fits for large scale group recommendation. The algorithm was also used in personalized information recommendation service system. The system could support information recommendation better. The results manifested that the algorithm was effective.