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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.


Papers
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Proceedings ArticleDOI
19 Jul 2010
TL;DR: The experimental results clearly show that the context-aware ranking approach improves the ranking of a commercial search engine which ignores context information and outperforms a baseline method which considers context information in ranking.
Abstract: The context of a search query often provides a search engine meaningful hints for answering the current query better. Previous studies on context-aware search were either focused on the development of context models or limited to a relatively small scale investigation under a controlled laboratory setting. Particularly, about context-aware ranking for Web search, the following two critical problems are largely remained unsolved. First, how can we take advantage of different types of contexts in ranking? Second, how can we integrate context information into a ranking model? In this paper, we tackle the above two essential problems analytically and empirically. We develop different ranking principles for different types of contexts. Moreover, we adopt a learning-to-rank approach and integrate the ranking principles into a state-of-the-art ranking model by encoding the context information as features of the model. We empirically test our approach using a large search log data set obtained from a major commercial search engine. Our evaluation uses both human judgments and implicit user click data. The experimental results clearly show that our context-aware ranking approach improves the ranking of a commercial search engine which ignores context information. Furthermore, our method outperforms a baseline method which considers context information in ranking.

181 citations

Journal ArticleDOI
TL;DR: A user study comparing three search interfaces shows that offering RTQE leads to better quality initial queries, more engagement in the search, and an increase in the uptake of query expansion, but also implies that care must be taken when implementingRTQE interactively.
Abstract: Interactive query expansion (IQE) (c.f. [Efthimiadis, E. N. (1996). Query expansion. Annual Review of Information Systems and Technology, 31, 121-187]) is a potentially useful technique to help searchers formulate improved query statements, and ultimately retrieve better search results. However, IQE is seldom used in operational settings. Two possible explanations for this are that IQE is generally not integrated into searchers' established information-seeking behaviors (e.g., examining lists of documents), and it may not be offered at a time in the search when it is needed most (i.e., during the initial query formulation). These challenges can be addressed by coupling IQE more closely with familiar search activities, rather than as a separate functionality that searchers must learn. In this article we introduce and evaluate a variant of IQE known as Real-Time Query Expansion (RTQE). As a searcher enters their query in a text box at the interface, RTQE provides a list of suggested additional query terms, in effect offering query expansion options while the query is formulated. To investigate how the technique is used - and when it may be useful - we conducted a user study comparing three search interfaces: a baseline interface with no query expansion support; an interface that provides expansion options during query entry, and a third interface that provides options after queries have been submitted to a search system. The results show that offering RTQE leads to better quality initial queries, more engagement in the search, and an increase in the uptake of query expansion. However, the results also imply that care must be taken when implementing RTQE interactively. Our findings have broad implications for how IQE should be offered, and form part of our research on the development of techniques to support the increased use of query expansion.

181 citations

Patent
31 Mar 2004
TL;DR: In this article, techniques are disclosed that locate implicitly defined semantic structures in a document, such as, for example, implicitly defined lists in an HTML document, which can be used in the calculation of distance values between terms in the documents.
Abstract: Techniques are disclosed that locate implicitly defined semantic structures in a document, such as, for example, implicitly defined lists in an HTML document. The semantic structures can be used in the calculation of distance values between terms in the documents. The distance values may be used, for example, in the generation of ranking scores that indicate a relevance level of the document to a search query.

181 citations

Journal ArticleDOI
TL;DR: A mobile recommendation system to answer popular location-related queries in daily life, and proposes three algorithms based on collaborative filtering which can consistently outperform the competing baselines and the newly proposed third algorithm can also outperform the authors' other two previous algorithms.

179 citations

Book
Hang Li1, Jun Xu1
20 Jun 2014
TL;DR: This survey gives a systematic and detailed introduction to newly developed machine learning technologies for query document matching (semantic matching) in search, particularly web search, and focuses on the fundamental problems, as well as the state-of-the-art solutions.
Abstract: Relevance is the most important factor to assure users' satisfaction in search and the success of a search engine heavily depends on its performance on relevance. It has been observed that most of the dissatisfaction cases in relevance are due to term mismatch between queries and documents (e.g., query "NY times" does not match well with a document only containing "New York Times"), because term matching, i.e., the bag-of-words approach, still functions as the main mechanism of modern search engines. It is not exaggerated to say, therefore, that mismatch between query and document poses the most critical challenge in search. Ideally, one would like to see query and document match with each other, if they are topically relevant. Recently, researchers have expended significant effort to address the problem. The major approach is to conduct semantic matching, i.e., to perform more query and document understanding to represent the meanings of them, and perform better matching between the enriched query and document representations. With the availability of large amounts of log data and advanced machine learning techniques, this becomes more feasible and significant progress has been made recently. This survey gives a systematic and detailed introduction to newly developed machine learning technologies for query document matching (semantic matching) in search, particularly web search. It focuses on the fundamental problems, as well as the state-of-the-art solutions of query document matching on form aspect, phrase aspect, word sense aspect, topic aspect, and structure aspect. The ideas and solutions explained may motivate industrial practitioners to turn the research results into products. The methods introduced and the discussions made may also stimulate academic researchers to find new research directions and approaches. Matching between query and document is not limited to search and similar problems can be found in question answering, online advertising, cross-language information retrieval, machine translation, recommender systems, link prediction, image annotation, drug design, and other applications, as the general task of matching between objects from two different spaces. The technologies introduced can be generalized into more general machine learning techniques, which is referred to as learning to match in this survey.

179 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