User Specific Search Using Grouping and Organization
Devang Karavadiya, Purnima Singh
Abstract
Devang Karavadiya, Purnima Singh
Abstract
The research overview described focuses on the design of search history displays to support information seeking (IS). Users are increasingly pursuing complex task- oriented goals on the Web, such as making travel arrangements, managing finances or planning purchases. Searchers create and use external records of their actions and the corresponding results by writing/typing notes, using copy and paste functions, and making printouts. To better support users in their long-term information quests on the Web, search engines keep track of their queries and clicks while searching online. In this paper, we study the problem of organizing a user's historical queries into groups in a dynamic and automated fashion. Automatically identifying query groups is helpful for a number of different search engine components and applications, such as query suggestions, result ranking, query alterations, sessionization, and collaborative search. We experimentally study the performance of different techniques, and showcase their potential, especially when combined together.
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The research overview described focuses on the design of search history displays to support information seeking (IS). Users are increasingly pursuing complex task- oriented goals on the Web, such as making travel arrangements, managing finances or planning purchases. Searchers create and use external records of their actions and the corresponding results by writing/typing notes, using copy and paste functions, and making printouts. To better support users in their long-term information quests on the Web, search engines keep track of their queries and clicks while searching online. In this paper, we study the problem of organizing a user's historical queries into groups in a dynamic and automated fashion. Automatically identifying query groups is helpful for a number of different search engine components and applications, such as query suggestions, result ranking, query alterations, sessionization, and collaborative search. We experimentally study the performance of different techniques, and showcase their potential, especially when combined together.
Key concepts: Computer science, Ranking (information retrieval), Task (project management), Information retrieval, Search engine, Web search query, World Wide Web, Web query classification