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QUERY RECOMMENDATION DEVICE, QUERY RECOMMENDATION METHOD, AND QUERY RECOMMENDATION PROGRAM

机译:查询推荐设备,查询推荐方法和查询推荐程序

摘要

PROBLEM TO BE SOLVED: To contribute to improvement in retrieval efficiency of a user by providing a recommended query of high precision for the user.;SOLUTION: An extraction part 3 sets position information of a query linked with a clicked URL in sequence and calculates the importance of the URL when extracting a retrieval log of each query linked with the URL from a storage part 2. A graph generation part 4 generates a click graph based upon the extracted retrieval log. A feature quantity extraction part 5 calculates a feature quantity between queries of the click graph. A graph generation part 6 generates a skip graph based upon the extracted retrieval log. A feature quantity extraction part 7 calculates a feature quantity between queries of the skip graph by using the distance between queries based upon the position information and the importance of the URL. A feature quantity composition part 8 calculates a feature quantity between queries by putting together the feature quantities calculated by the parts 5 and 7. A recommended query is presented to the user according to the feature quantity.;COPYRIGHT: (C)2012,JPO&INPIT
机译:解决的问题:通过向用户提供高精度的推荐查询来帮助提高用户的检索效率。解决方案:提取部分3依次设置与单击的URL链接的查询的位置信息,并计算从存储部分2中提取与URL链接的每个查询的检索日志时URL的重要性。图形生成部分4根据提取的检索日志生成点击图形。特征量提取部5计算点击图的查询之间的特征量。图生成部6基于提取出的检索日志来生成跳过图。特征量提取部分7基于位置信息和URL的重要性,通过使用查询之间的距离来计算跳过图的查询之间的特征量。特征量组成部分8通过将由部分5和7计算出的特征量放在一起来计算查询之间的特征量。根据特征量向用户提供推荐的查询。版权所有:(C)2012,JPO&INPIT

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