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Search Engine Results Ranking Based On Web Page Clustering
Author: SunShanShan
Tutor: SuoHongGuangï¼›LiangYuHuan
School: China University of Petroleum
Course: Computer Science and Technology
Keywords: Search Engine Text Clustering Personalized Ranking User interest
CLC: TP391.3
Type: Master's thesis
Year: 2010
Downloads: 186
Quote: 2
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Abstract
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As Web resources constantly enrich the user tries to query information through the search engine . However, users experience the convenience brought by the information retrieval system , also come to understand the difficulties of access to information . On the one hand , the search engine is mainly based on the query glyph match the return of a large number of hits , the query with a wide range of semantics , so mixed theme present in the list of results returned in the search process , the user must keep the results screening, spent a lot of time . On the other hand , users get search results with personalization. In response to these problems , we propose a sort of web-based clustering search engine results . First, in order to solve the search engine returns results theme confounding phenomenon , to help users quickly and accurately locate valuable information , text clustering applied to the processing of search results is proposed based on the theme phrase search engine results clustering method . Extraction characteristics of the returned results , a new feature extraction method , the theme phrase feature vector constituted by the query keywords and high-frequency independent word . While introducing Cilin the semantic expansion feature items , improved k-means clustering algorithm to cluster the search results , and the extraction category labels for each category . Secondly , for users to retrieve personalized search results sorted based on user interest and the clustering of the page . By mining user interest , the establishment of the interest model , Sort By clustering results based on user interest , and at the same time expand the category labels based on user interest model , and fine-tune the order of pages within the category of interest to the user comprehensive indicators . Finally , according to the algorithm thought the experimental test , the experimental data were analyzed . Experimental results show that the search engine results based on the theme phrase clustering algorithm can effectively improve the precision of the clustering results , clustering category refine the query topics . Sort improves user efficiency and accurate access to information based on user interest . The system there are a lot of inadequacies need to be further improved .
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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Retrieval machine
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