Research And Development Of Search Engine Based On Focus Relevance Ranking

Search engine is the most important tool for people to get useful information from the magnanimity web data,also it is the key content of researching and developing web information. But currently,with the web information's blast increasing and multivariant information's developing,it comes to be more and more difficult to retrieve desirable information speediness and effectively. Traditional search engine can't meets users' high precision requirement of searching information, vertical search engine ,which is professional and oriented topic,becoming the research hot spot.Relevance ranking technology is the core technology of the vertical search engine, it plays an important role in retrieving topic data and providing relevance searching result.The paper works on research the key issues of relevance ranking technology of vertical search engine,and describes improved model and algorithm of the topic crawling technology, ranking bases on links structure,ranking bases on page weight and so on.Improve the quality of relevance ranking to improve the performance of vertical search engine.Finally,design and develop a vertical search engine orient domain.The main contributions of the paper include:(1)Aim at the problem of topic crawler can't get through the dark tunnel,use online learning method and assist function to improve the topic crawling strategy of topic crawler,make it can retrieve high relevance topic data. (2)Research PageRank algorithm and its'improve algorithms,through modeling of the behavior of user click page,improve the way of delivering PageRank value between links,and describes the improve algorithm,which does't need added space ,can prevent the topic drift event from happening.(3)Aim at the high dimension shortcoming of feature distill model, describes the customization method of page weight to constitute the factor of page weight,and use dissoluble criterion to weigh the page weight factors,and get the evaluate function which can reduce the dimension of feature vector.,(4)Describes the focus relevance ranking strategy to integrate the three aspect improvement above,and put it into practice in the development of search engine.(5)Using the Lucene full text search engine framwork to develop a oriented automobile topic search engine system. The Pratical Application shows , our focus relevance ranking strategy makes the search engine have improvement in relevance, recall ratio and precision ratio.