Research On Vertical Search Engine Based On Lucene

The concept of "big data" is well known and familiar from John R.Mashey in 1998.In the face of such a huge amount of information,general search has done well,but its results are large,miscellaneous,and wide.It often requires us to page many times to find information that we are interested in and take time and effort.So this article Through the in-depth study of vertical engine and recommendation algorithm,a vertical search engine based on user personalization needs is realized,which makes it more fast to locate the information set we are really interested in,and to reduce the number of pages to turn the page with the sorting algorithm and the recommendation algorithm,from a relatively small number of searches and pages.In order to meet the user's information needs,this paper optimizes the sorting algorithm on the basis of the Lucene score,which is based on the in-depth study of the vertical search engine.In order to order the result more in accordance with the user preferences,this paper adds a user interest cooling algorithm on the basis of collecting user interest sets,which can more accurately locate the user's stage preference.In order to excavate the potential needs of users,an adaptive user interest mining recommendation algorithm is proposed.The sorting and recommending algorithms complement each other to fully excavate the information needed by users to form the overall personalized sort and recommendation and build the implementation.A personalized vertical search engine system.The main work of this paper is the vertical search of goods.In view of the huge problem of massive information storage cable index,we adopt the solution of element extraction and distributed storage technology search.In view of the problem of vertical search "stiff" problem,the two order algorithm based on user interest is proposed and the potential demand of users is excavated.The development of distributed computing and storage has been improved to solve the problem of massive information storage,especially the scalability and disaster tolerance greatly improve the security of the data.In this paper,the principle and application scenes of vertical search engines have been studied and studied in depth,combined with full text retrieval workers.The excellent distributed framework ElasticSearch with Lucene designed and implemented a vertical search engine system for commodity direction.Through the study of the Lucene scoring mechanism,the retrieval sorting algorithm was improved,the sorting algorithm was optimized,and two interest related ordering was carried out on the basis of the first comprehensive attribute sorting.The adaptive user interest mining recommendation algorithm is introduced,and the recommendation algorithm adds the influence factor of the user's geographic location parameter.It combines the Rocchio recommendation algorithm and the collaborative filtering recommendation algorithm based on the JLH significant score and the mixed recommendation algorithm made by the commodity heat recommendation algorithm.Personalized recommendation and vertical search personalized sorting results.