Recommender System Based On Web Mining Web Page Dynamic

It is meaningful to extract user navigation model by utilizing web data mining: pre-fetching webpage while user access the website, recommending goods to the user in the scenario of e-business and optimizing the structure of the website. However, under the environment of information exploding, the content of the website or the behavior of user navigation is changing at any given time. All this require a high standard for the designing of webpage recommendation system.In order to predict which page the user would need in the next step, the recommendation system need to reference to the pages which had been navigated before. Since sequence model take the page's navigation history into consideration, this paper take the related theory of sequence model as foundation. In the domain of user navigation model based on sequence model, the prevalent models are Markov model and PLSA model.But after detailed analysis, these two models have defects when handle the problem under the condition that the content of the website and the behavior of user navigation are changing.This article first introduces the current situation of this domain and the common process of web data mining. It gives a filtering way to preprocess the web log data. For the webpage aggregation, this article introduces several existing methods and then proposes two ways based on URL to solve this problem on the premise that the structure of the website is sound:based on the distance between two URL and based on the path of URL tree.Since the way based on the distance between tow URL can't adapt to the dynamic changing situation, this paper will take the later method. For extracting of the sequence model, it point out the flaws of PLSA and then propose RTA algorithm which is base on path tree. Also, this article tells how to update the recommendation system.Then it gives a solution to designing the webpage recommendation system, which based on the behavior of user navigation.This article employs hit ratio to rate the recommendation system. At the end of this article, the experiment shows the relationship between the number of recommendation pages,the support degree,the length of sliding window and the hit ratio. The result proves that PTS is better than PLSA under a specific condition.