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.
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25 Oct 2010TL;DR: The goal of automatically ranking photographs is not intended for award-wining professional photographs but for photographs taken by amateurs, especially when individual preference is taken into account.
Abstract: In this paper, we propose a novel personalized ranking system for amateur photographs. Although some of the features used in our system are similar to previous work, new features, such as texture, RGB color, portrait (through face detection), and black-and-white, are included for individual preferences. Our goal of automatically ranking photographs is not intended for award-wining professional photographs but for photographs taken by amateurs, especially when individual preference is taken into account. The performance of our system in terms of precision-recall diagram and binary classification accuracy (93%) is close to the best results to date for both overall system and individual features. Two personalized ranking user interfaces are provided: one is feature-based and the other is example-based. Although both interfaces are effective in providing personalized preferences, our user study showed that example-based was preferred by twice as many people as feature-based.
104 citations
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21 Oct 2005TL;DR: In this article, the structured content and associated metadata from the Web are leveraged to provide specific answer string responses to user questions, which can also be indexed at crawl-time to facilitate searching of the content at search-time.
Abstract: Structured content and associated metadata from the Web are leveraged to provide specific answer string responses to user questions. The structured content can also be indexed at crawl-time to facilitate searching of the content at search-time. Ranking techniques can also be employed to facilitate in providing an optimum answer string and/or a top K list of answer strings for a query. Ranking can be based on trainable algorithms that utilize feature vectors for candidate answer strings. In one instance, at crawl-time, structured content is indexed and automatically associated with metadata relating to the structured content and the source web page. At search-time, candidate indexed structured content is then utilized to extract an appropriate answer string in response to a user query.
104 citations
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16 Jun 2012TL;DR: It is shown that for one-vs-rest, learning through cross-validation the optimal degree of imbalance between the positive and the negative samples can have a significant impact and early stopping can be used as an effective regularization strategy when training with stochastic gradient algorithms.
Abstract: We propose a benchmark of several objective functions for large-scale image classification: we compare the one-vs-rest, multiclass, ranking and weighted average ranking SVMs. Using stochastic gradient descent optimization, we can scale the learning to millions of images and thousands of classes. Our experimental evaluation shows that ranking based algorithms do not outperform a one-vs-rest strategy and that the gap between the different algorithms reduces in case of high-dimensional data. We also show that for one-vs-rest, learning through cross-validation the optimal degree of imbalance between the positive and the negative samples can have a significant impact. Furthermore, early stopping can be used as an effective regularization strategy when training with stochastic gradient algorithms. Following these "good practices", we were able to improve the state-of-the-art on a large subset of 10K classes and 9M of images of lmageNet from 16.7% accuracy to 19.1%.
104 citations
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01 Jul 2000TL;DR: Search effectiveness when using query-based Internet search, directory-based search and phrase-based query reformulation assisted search is compared by means of a controlled, user-based experimental study.
Abstract: This article compares search effectiveness when using query-based Internet search (via the Google search engine), directory-based search (via Yahoo) and phrase-based query reformulation assisted search (via the Hyperindex browser) by means of a controlled, user-based experimental study. The focus was to evaluate aspects of the search process. Cognitive load was measured using a secondary digit-monitoring task to quantify the effort of the user in various search states; independent relevance judgements were employed to gauge the quality of the documents accessed during the search process. Time was monitored in various search states. Results indicated the directory-based search does not offer increased relevance over the query-based search (with or without query formulation assistance), and also takes longer. Query reformulation does significantly improve the relevance of the documents through which the user must trawl versus standard query-based internet search. However, the improvement in document relevance comes at the cost of increased search time and increased cognitive load.
104 citations
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30 Nov 2005TL;DR: In this paper, an adaptive semantic reasoning engine that receives a natural language query, which may contain one or more contexts, is broken down into tokens or a set of tokens, and a task search can be performed on the token or token set(s) to classify a particular query and/or context and retrieve one and more tasks.
Abstract: Provided is an adaptive semantic reasoning engine that receives a natural language query, which may contain one or more contexts. The query can be broken down into tokens or a set of tokens. A task search can be performed on the token or token set(s) to classify a particular query and/or context and retrieve one or more tasks. The token or token set(s) can be mapped into slots to retrieve one or more task result. A slot filling goodness may be determined that can include scoring each task search result and/or ranking the results in a different order than the order in which the tasks were retrieved. The token or token set(s), retrieved tasks, slot filling goodness, natural language query, context, search result score and/or result ranking can be feedback to the reasoning engine for further processing and/or machine learning.
104 citations