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Exploiting social bookmarking services to build clustered user interest profile for personalized search

机译：利用社交书签服务来构建群集的用户兴趣档案，以进行个性化搜索

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摘要

Search engine users tend to write short queries, generally comprising of two or three query words. As these queries are often ambiguous or incomplete, search engines tend to return results whose rankings reflect a community of intent. Moreover, search engines are designed to satisfy the needs of the general populace, not those of a specific searcher. To address these issues, we propose two methods that use Singular Value Decomposition (SVD) to build a Clustered User Interest Profile (CUIP), for each user, from the tags annotated by a community of users to web resources of interest. A CUIP consists of clusters of semantically or syntactically related tags, each cluster identifying a topic of the user's interest. The matching cluster, to the given user's query, aids in disambiguation of user search needs and assists the search engine to generate a set of personalized search results. A series of experiments was executed against two data sets to judge the clustering tendency of the cluster structure CUIP, and to evaluate the quality of personalized search. The experiment results indicate that the CUIP based personalized search outperforms the baseline search and is better than the other approaches that use social bookmarking services for building a user profile and use it for personalized search.
机译：搜索引擎用户倾向于编写简短的查询，通常由两个或三个查询词组成。由于这些查询通常是模棱两可或不完整的，因此搜索引擎趋向于返回其排名反映出意图社区的结果。此外，搜索引擎旨在满足一般人群的需求，而不是特定搜索者的需求。为了解决这些问题，我们提出了两种使用奇异值分解（SVD）来为每个用户从用户社区注释的标签到感兴趣的Web资源构建聚类的用户兴趣配置文件（CUIP）的方法。 CUIP由语义或句法相关标签的群集组成，每个群集标识用户感兴趣的主题。对于给定用户的查询，匹配的集群有助于消除用户搜索需求的歧义，并帮助搜索引擎生成一组个性化搜索结果。针对两个数据集执行了一系列实验，以判断聚类结构CUIP的聚类趋势，并评估个性化搜索的质量。实验结果表明，基于CUIP的个性化搜索性能优于基准搜索，并且优于使用社交书签服务构建用户资料并将其用于个性化搜索的其他方法。

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