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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06 Nov 2007TL;DR: It is concluded that known schemes to release even heavily scrubbed query logs that contain session information have significant privacy risks.
Abstract: We investigate the subtle cues to user identity that may be exploited in attacks on the privacy of users in web search query logs. We study the application of simple classifiers to map a sequence of queries into the gender, age, and location of the user issuing the queries. We then show how these classifiers may be carefully combined at multiple granularities to map a sequence of queries into a set of candidate users that is 300-600 times smaller than random chance would allow. We show that this approach remains accurate even after removing personally identifiable information such as names/numbers or limiting the size of the query log.We also present a new attack in which a real-world acquaintance of a user attempts to identify that user in a large query log, using personal information. We show that combinations of small pieces of information about terms a user would probably search for can be highly effective in identifying the sessions of that user.We conclude that known schemes to release even heavily scrubbed query logs that contain session information have significant privacy risks.
184 citations
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05 Jun 1985TL;DR: The results indicate that while the absolute performance of a search on a particular collection is dependent on the pairwise similarity of the relevant documents, the relative effectiveness of clustered retrieval versus sequential retrieval is independent of this factor.
Abstract: A new means of evaluating the cluster hypothesis is introduced and the results of such an evaluation are presented for four collections. The results of retrieval experiments comparing a sequential search, a cluster-based search, and a search of the clustered collection in which individual documents are scored against the query are also presented. These results indicate that while the absolute performance of a search on a particular collection is dependent on the pairwise similarity of the relevant documents, the relative effectiveness of clustered retrieval versus sequential retrieval is independent of this factor. However, retrieval of entire clusters in response to a query usually results in a poorer performance than retrieval of individual documents from clusters.
183 citations
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183 citations
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TL;DR: A ranking aggregation algorithm is proposed to enhance the detection of similarity and dissimilarity based on the following assumption: the true match should be similar to the probe in different baseline methods, but also be dissimilar to those strongly dissimilar galleries of the probe.
Abstract: Person reidentification is a key technique to match different persons observed in nonoverlapping camera views. Many researchers treat it as a special object-retrieval problem, where ranking optimization plays an important role. Existing ranking optimization methods mainly utilize the similarity relationship between the probe and gallery images to optimize the original ranking list, but seldom consider the important dissimilarity relationship. In this paper, we propose to use both similarity and dissimilarity cues in a ranking optimization framework for person reidentification. Its core idea is that the true match should not only be similar to those strongly similar galleries of the probe, but also be dissimilar to those strongly dissimilar galleries of the probe. Furthermore, motivated by the philosophy of multiview verification, a ranking aggregation algorithm is proposed to enhance the detection of similarity and dissimilarity based on the following assumption: the true match should be similar to the probe in different baseline methods. In other words, if a gallery blue image is strongly similar to the probe in one method, while simultaneously strongly dissimilar to the probe in another method, it will probably be a wrong match of the probe. Extensive experiments conducted on public benchmark datasets and comparisons with different baseline methods have shown the great superiority of the proposed ranking optimization method.
183 citations