Liliana Calderón-Benavides

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The identification of the user's intention or interest through queries that they submit to a search engine can be very useful to offer them more adequate results. In this work we present a framework for the identification of user's interest in an automatic way, based on the analysis of query logs. This identification is made from two perspectives, the(More)
The identification of the user's intent behind a web query is considered to be one of the most important challenges for the modern information retrieval systems. Although there have been studies that characterize some possible dimensions for the user intents, most of these studies just place each query into one or two dimensions. In order to approach to the(More)
In this paper we process and analyze web search engine query and click data from the perspective of the query session (query + clicked results) conducted by the user. We initially state some hypotheses for possible user types and quality profiles for the user session, based on descriptive variables of the session. The query dataset is preprocessed and(More)
The identification of a user's intention or interest by the analysis of the queries submitted to a search engine and the documents selected as answers to these queries, can be very useful to offer more adequate results for that user. In this Chapter we present the analysis of a Web search engine query log from two different perspectives: the query session(More)
The problem of identifying user intent has received considerable attention in recent years, particularly in the context of improving the search experience via query contextualization. Intent can be characterized by multiple dimensions, which are often not observed from query words alone. Accurate identification of Intent from query words remains a(More)
In this paper we process and analyze Web search engine query and click data from the perspective of the documents (URs) selected. We initially define possible document categories and select descriptive variables to define the documents. The URL dataset is preprocessed and analyzed using some traditional statistical methods, and then processed by the Kohonen(More)
Nowadays, offer more precise and reliable information to users, according with their likes, is a topic which generate great interest not only for the research community but enterprises too. Recommender systems are based in techniques, such as collaborative filtering, to present to users those items which, according with different metrics and based in their(More)
Understanding the different ways of the information seeking behavior of users, and their implications is an important challenge of Web information services. In this paper we examine the suitability of Web search engines (SE) and community question-answering (community QA) to satisfy the different expectations of users: queries expecting knowledge-based(More)
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