The Research On The User Interest Model In Personal Service

With the great increase of data on the Internet in recent years, people's demand for data becomes more and more specialized. The personalized service technology can be applied to remove the contradiction between the diversification of information and the specialization of the users'demand. As a central concern in personalized service, the Users'Interest Construction Model Technology mainly aims to probe into how to organize the users'interest sources effectively and how to indicate, upgrade, store and calculate the users'interest. This dissertation plans to make a study from the following aspects and tries to prove the effectiveness of the technology by conducting a series of experiments. First, Proposition of the question . Through a large amount of researches of traditional user interest model, this thesis have proposed a new method of categorized user interest which is different from the method based on Standard class tree or files discipline aggregation, that is the direct categorized method based on the user browsed page. Second, Collection of initial data. It is to collect user page set and user behavior record in pages that data collection works. The collection of page is mainly done through client-side and sever side.Frist is to standardize various kinds of files collected .Second is to clean the page, which mainly removes stop words and various kinds of file labels in pages, and counts the frequency of the character words based on the abstraction of the character words by the field dictionary. The user behavior data is collected through a design of browser plug which can catch user behavior data in client-side,and counts and keeps the operation array of this page when user finishing browse each time. Third, Calculation of the character weight.The calculation of the character weight based on page content adopts the TF-IDF-IG formula, while the calculation of the interest degree of user behavior is based on the regressive Analysis equation of user behaviouser behavior.Then on the basis of the interest degree , the integrated vectorial feature files character weight is calculated by the combination of the page content data and user behavior equation. Finally, integrated vectorial feature matrix of the page is drawn out. Fourth, Formulation of user interest model. Combined Level Clustering is proposed by comparing the level Clustering and k-means Clustering method. This algorithm effectively avoids the disadvantage of the amalgamation or division of the level Clustering,and prevents k-means clustering from being apt to solve somly and optimumly. The validity is also proved by experiments. User interest model that this thesis studies can be used in the field of user's individualized information service, customer information management, e-commerce, and datum excavate, so this research is of good applying value and reference value in real life.