Abstract

With the development of the Internet, web service generates a large amount of log information, how to mine user preferred browsing paths is an important research areas. Current researches mainly focus on the mining of user preferred browsing paths, however, they do not delve into the personalization of preferred paths and lack of semantic information. To provide personalized preferred paths to fulfill user need, in this paper, we proposed a novel method to compute the similarities of preferred paths and the given fields by experts. Firstly, the similarities of each page on the preferred paths and the given fields are computed. Secondly, according to the computed similarities of each page on the preferred paths and the given fields, the average similarity of all the pages on the preferred path and the the given field is computed, and it is used as the similarity of preferred path and the given field. After the processing mentioned above, the website can automatically recommend the related preferred paths to 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.