David J. Brenes

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User interactions with search engines reveal three main underlying intents, namely <i>navigational, informational</i>, and <i>transactional.</i> By providing more accurate results depending on such query intents the performance of search engines can be greatly improved. Therefore, query classification has been an active research topic for the last years.(More)
Online Social Networks (OSNs) are a cutting edge topic. Almost everybody –users, marketers, brands, companies, and researchers– is approaching OSNs to better understand them and take advantage of their benefits. Maybe one of the key concepts underlying OSNs is that of influence which is highly related, although not entirely identical, to those of popularity(More)
One of the most important issues in Information Retrieval is inferring the intents underlying users’ queries. Thus, any tool to enrich or to better contextualized queries can proof extremely valuable. Entity extraction, provided it is done fast, can be one of such tools. Such techniques usually rely on a prior training phase involving large datasets. That(More)
Characterizing user’s intent and behaviour while using a retrieval information tool (e.g a search engine) is a key question on web research, as it hold the keys to know how the users interact, what they are expecting and how we can provide them information in the most beneficial way. Previous research has focused on identifying the average characteristics(More)
One of the main interests in the Web Information Retrieval research area is the identification of the user interests and needs so the search engines and tools can help the users to improve their efficiency. Some research has been done on automatically identifying the goals of the queries the user submits, but it is usually based in semantic or syntactic(More)
Search engines are nowadays one of the most important entry points for Internet users and a central tool to solve most of their information needs. Still, there exist a substantial amount of users’ searches which obtain unsatisfactory results. Needless to say, several lines of research aim to increase the relevancy of the results users retrieve. In this(More)
The Query Intent classification using semi-supervised learning about ti find a better away to search the web precision that result surfer want to search is 99. 8% matched, but due to search engine know what type of query user want to search and logs that are residing in the server of search engine . Which are put in data warehouse of vendor search engine(More)
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