A New Algorithm for Inferring User Search Goals with Feedback Sessions
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Year of Project2013
A New Algorithm for Inferring User Search Goals with Feedback Sessions IEEE Projects 2013 | Final year projects | BE Projects | Abstract: For a broad-topic and ambiguous query, different users may have different search goals when they submit it to a search engine. The inference and analysis of user search goals can be very useful in improving search engine relevance and user experience. In this paper, we propose a novel approach to infer user search goals by analyzing search engine query logs. First, we propose a framework to discover different user search goals for a query by clustering the proposed feedback sessions. Feedback sessions are constructed from user click-through logs and can efficiently reflect the information needs of users. Second, we propose a novel approach to generate pseudo-documents to better represent the feedback sessions for clustering. Finally, we propose a new criterion )“Classified Average Precision (CAP)” to evaluate the performance of inferring user search goals. Experimental results are presented using user click-through logs from a commercial search engine to validate the effectiveness of our proposed methods.
A New Algorithm for Inferring User Search Goals with Feedback Sessions. IN web search applications, queries are submitted to search engines to represent the information needs of users. However, sometimes queries may not exactly represent users’ specific information needs since many ambiguous queries may cover a broad topic and different users may want to get information on different aspects when they submit the same query. For example, when the query “the sun” is submitted to a search engine, some users want to locate the homepage of a United Kingdom newspaper, while some others want to learn the natural knowledge of the sun, as shown in Fig. 1. Therefore, it is necessary and potential to capture different user search goals in information retrieval. We define user search goals as the information on different aspects of a query that user groups want to obtain. Information need is a user’s particular desire to obtain information to satisfy his/her need. User search goals can be considered as the clusters of information needsmfor a query. The inference and analysis of user search goals can have a lot of advantages in improving search engine relevance and user experience. Some advantages are summarized as follows. First, we can restructure web search results according to user search goals by grouping the search results with the same search goal; thus, users with different search goals can easily find what they want. Second, user search goals represented by some keywords can be utilized in query recommendation, thus, the suggested queries can help users to form their queries more precisely. Third, the distributions of user search goals can also be useful in applications such as reranking web search results that contain different user search goals.
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