Mining Web Access Log for the Personalization Recommendation

This paper presents a personalization recommenddation model to recommend potentially interesting resources to users based on the web access log of users. This model builds on the apriori algorithm and the tf-idf technology, which consists of three parts: resource description, user's preference extraction and the personalization recommendation. Firstly our model generates resource text space vector by analyzing the resource information achieved by raining user's web access log, then it attains interest set to make use of the apriori algorithm based on the vector, finally, it recommends filtered and sorted resources to users content based recommendation model.