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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.


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Proceedings ArticleDOI
21 Jul 2017
TL;DR: A novel memory-augmented Attribute Manipulation Network (AMNet) which can manipulate image representation at the attribute level and achieve remarkably good performance compared with well-designed baselines in terms of effectiveness of attribute manipulation and search accuracy.
Abstract: We introduce a new fashion search protocol where attribute manipulation is allowed within the interaction between users and search engines, e.g. manipulating the color attribute of the clothing from red to blue. It is particularly useful for image-based search when the query image cannot perfectly match users expectation of the desired product. To build such a search engine, we propose a novel memory-augmented Attribute Manipulation Network (AMNet) which can manipulate image representation at the attribute level. Given a query image and some attributes that need to modify, AMNet can manipulate the intermediate representation encoding the unwanted attributes and change them to the desired ones through following four novel components: (1) a dual-path CNN architecture for discriminative deep attribute representation learning, (2) a memory block with an internal memory and a neural controller for prototype attribute representation learning and hosting, (3) an attribute manipulation network to modify the representation of the query image with the prototype feature retrieved from the memory block, (4) a loss layer which jointly optimizes the attribute classification loss and a triplet ranking loss over triplet images for facilitating precise attribute manipulation and image retrieving. Extensive experiments conducted on two large-scale fashion search datasets, i.e. DARN and DeepFashion, have demonstrated that AMNet is able to achieve remarkably good performance compared with well-designed baselines in terms of effectiveness of attribute manipulation and search accuracy.

144 citations

Patent
Gianluca Gargaro1, Andrea Orestano1
12 Mar 2009
TL;DR: An apparatus and method for implementing a collaborative search engine is described in this article. But it does not address the problem of identifying a plurality of users that are associated with information relevant to one or more of the search tags.
Abstract: An apparatus and method for implementing a collaborative search engine. A plurality of search tags associated with one or more search terms received from a client device associated with a user of a collaborative search engine are determined. A plurality of users of the collaborative search engine that are associated with information relevant to one or more of the search tags are identified. A time reference associated with the information associated with each of the users is determined. Search results are generated ranking the relevance of each of the plurality of users with respect to the search tags based, at least in part, on the relevance of the information associated with each of the users and the time reference associated with the information.

144 citations

Proceedings ArticleDOI
01 Sep 2001
TL;DR: Experimental results show that the two-level cache is superior, and that it allows increasing the maximum number of queries processed per second by a factor of three, while preserving the response time.
Abstract: We present an e ective caching scheme that reduces the computing and I/O requirements of a Web search engine without altering its ranking characteristics. The novelty is a two-level caching scheme that simultaneously combines cached query results and cached inverted lists on a real case search engine. A set of log queries are used to measure and compare the performance and the scalability of the search engine with no cache, with the cache for query results, with the cache for inverted lists, and with the two-level cache. Experimental results show that the two-level cache is superior, and that it allows increasing the maximum number of queries processed per second by a factor of three, while preserving the response time. These results are new, have not been reported before, and demonstrate the importance of advanced caching schemes for real case search engines.

144 citations

Proceedings ArticleDOI
31 Oct 2004
TL;DR: This paper presents a general probabilistic technique for error ranking that exploits correlation behavior amongst reports and incorporates user feedback into the ranking process and observes a factor of 2-8 improvement over randomized ranking for error reports emitted by both intra-Procedural and inter-procedural analysis tools.
Abstract: Static program checking tools can find many serious bugs in software, but due to analysis limitations they also frequently emit false error reports. Such false positives can easily render the error checker useless by hiding real errors amidst the false. Effective error report ranking schemes mitigate the problem of false positives by suppressing them during the report inspection process [17, 19, 20]. In this way, ranking techniques provide a complementary method to increasing the precision of the analysis results of a checking tool. A weakness of previous ranking schemes, however, is that they produce static rankings that do not adapt as reports are inspected, ignoring useful correlations amongst reports. This paper addresses this weakness with two main contributions. First, we observe that both bugs and false positives frequently cluster by code locality. We analyze clustering behavior in historical bug data from two large systems and show how clustering can be exploited to greatly improve error report ranking. Second, we present a general probabilistic technique for error ranking that (1) exploits correlation behavior amongst reports and (2) incorporates user feedback into the ranking process. In our results we observe a factor of 2-8 improvement over randomized ranking for error reports emitted by both intra-procedural and inter-procedural analysis tools.

144 citations

Book
01 Jan 1978
TL;DR: The majority of the persons in this book had the advantage of being born and raised in centers of civilization, highly cultured or politically pivotal nations as mentioned in this paper, whereas Muhammad, however, was born in the year 570, in the city of Mecca, in southern Arabia, at that time a backward area of the world, far from the centers of trade, art, and learning.
Abstract: The majority of the persons in this book had the advantage of being born and raised in centers of civilization, highly cultured or politically pivotal nations. Muhammad, however, was born in the year 570, in the city of Mecca, in southern Arabia, at that time a backward area of the world, far from the centers of trade, art, and learning. Orphaned at age six, he was reared in modest surroundings. Islamic tradition tells us that he was illiterate. His economic position improved when, at age twenty-five, he married a wealthy widow. Nevertheless, as he approached forty, there was little outward indication that he was a remarkable person.

144 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168