Personalized Search Engine Based On User Interest Model, Research And Analysis

As the Internet increasingly rapid development, it gives us a mass of information, but also provides a lot of useful information, but we have to so much information in the query the information we need be also very difficult. Search engine become the most commonly used information retrieval tool, it involves a database, information retrieval, artificial only, data mining, Java, natural language processing and other fields of theory and technology. Traditional search engine results retrieved for any users are the same, without taking into account the user's interest in the issue. Therefore, search results often do not meet the needs of users, users often require vast amounts of search results to filter the information they need, so spend a lot of time. Therefore, the personalized search engine is currently an important research topic. In this paper, the current lack of search engine, fully study the development of personalized search engines situation.The main work of this paper is as follows:1) Search engine, search engine processes, historical development, current situation and existing problems are reviewed.2) Personalized search engines and related technologies, and Web data mining has been studied.3) Introduced the description of user interest model, create, and update.4) The traditional personalized PageRank algorithm, keyword-based user transaction sequence clustering model, is given personalized PageRank algorithm to calculate the user personalized information characterization.5) Gives a more complete model of personalized search engine system, and the sequence of the user based on keywords pattern recognition, and personalized PageRank method gives the comparison of the results.This study and exploration of the search engines for further research related to technology, a development proposed personalized search engine solution.