Topic
Ranking (information retrieval)
About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.
Papers published on a yearly basis
Papers
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10 Jun 1998TL;DR: In this paper, a knowledge base comprising a plurality of nodes of terminology, arranged hierarchically, that reflect associations among the terminology is used to generate hierarchical query feedback to facilitate the user in reformulating the query.
Abstract: An information retrieval system generates hierarchical query feedback to a user to facilitate the user in reformulating the query. The information retrieval system, which supports both text and theme queries, includes a knowledge base comprising a plurality of nodes of terminology, arranged hierarchically, that reflect associations among the terminology. For the hierarchical query feedback terms, the information retrieval system selects terminology that broadens and narrows the query terms by selecting parent nodes and child nodes, respectively, of the nodes for terminology that corresponds to the terms of the query. The information retrieval system also selects terminology that is generally related to the query terms by selecting nodes of the knowledge base that are cross linked to the nodes for terminology that corresponds to the terms of the query. Normalization processing, which generates canonical forms for query processing, and a content processing system, which generates themes for theme queries, are also disclosed.
144 citations
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31 Aug 2006TL;DR: In this paper, a recommendation system identifies users who are related to the target user through no more than a maximum degree of separation, and then ranks the identified users based on a likelihood that the target users will want to have a direct relationship with those identified users.
Abstract: A method and system for recommending potential contacts to a target user is provided. A recommendation system identifies users who are related to the target user through no more than a maximum degree of separation. The recommendation system identifies the users by starting with the contacts of the target user and identifying users who are contacts of the target user's contacts, contacts of those contacts, and so on. The recommendation system then ranks the identified users, who are potential contacts for the target user, based on a likelihood that the target user will want to have a direct relationship with the identified users. The recommendation system then presents to the target user a ranking of the users who have not been filtered out.
144 citations
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144 citations
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16 Apr 2012TL;DR: A temporal modeling framework adapted from physics and signal processing that can be used to predict time-varying user behavior using smoothing and trends and a novel learning algorithm that explicitly learns when to apply a given prediction model among a set of such models.
Abstract: User behavior on the Web changes over time. For example, the queries that people issue to search engines, and the underlying informational goals behind the queries vary over time. In this paper, we examine how to model and predict this temporal user behavior. We develop a temporal modeling framework adapted from physics and signal processing that can be used to predict time-varying user behavior using smoothing and trends. We also explore other dynamics of Web behaviors, such as the detection of periodicities and surprises. We develop a learning procedure that can be used to construct models of users' activities based on features of current and historical behaviors. The results of experiments indicate that by using our framework to predict user behavior, we can achieve significant improvements in prediction compared to baseline models that weight historical evidence the same for all queries. We also develop a novel learning algorithm that explicitly learns when to apply a given prediction model among a set of such models. Our improved temporal modeling of user behavior can be used to enhance query suggestions, crawling policies, and result ranking.
143 citations
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TL;DR: An O(n log n) time algorithm to find an optimal ranking of an n-node tree is described.
143 citations