Personalized mining of preferred paths based on web log

With the development of the Internet, web service generates a large amount of log information, how to mine user preferred browsing paths from web log information is an important research area. Current researches mainly focus on the mining of user preferred browsing paths, however, they do not delve into the personalization of preferred paths and paths lack semantic information. To provide personalized preferred paths to fulfill users need, this paper proposes a innovative method of user preferred browsing path analysis based on vector space model. Firstly, path eigenvectors are adopted to denote the obtained preferred paths, and field eigenvectors are given by field experts. Secondly, the cosine similarity of path eigenvectors and field eigenvectors are computed. Thirdly, the preferred paths are partitioned into clusters according to the cosine similarity. Finally, the clusters are annotated using related fields. After clustering and annotation, the website can automatically recommend the related preferred paths for users according to the choice of users. Experiments show that it is accurate and scalable. It can be applied to optimize website or design personalized service.