An effective query recommendation approach using semantic strategies for intelligent information retrieval

Song, Wei; Liang, Jiuzhen; Cao, Xiao; Park, Soon Cheol

doi:10.1016/j.eswa.2013.07.052

Cited by 16 publications

(4 citation statements)

References 37 publications

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“…As a baseline, we hypothesize that the top matching results from a commercial search engine will provide a useful source of query suggestions, e.g. (Kruschwitz et al, 2009;Song et al, 2014). We extracted noun phrases from the top ten snippets returned by a popular Web search engine, in this case Google, restricting the search to domain-specific sites, e.g.…”

Section: Search Results Snippetsmentioning

confidence: 99%

Interactive query expansion for professional search applications

Russell-Rose

Gooch²,

Kruschwitz

2021

Business Information Review

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Knowledge workers (such as healthcare information professionals, patent agents and recruitment professionals) undertake work tasks where search forms a core part of their duties. In these instances, the search task is often complex and time-consuming and requires specialist expert knowledge to formulate accurate search strategies. Interactive features such as query expansion can play a key role in supporting these tasks. However, generating query suggestions within a professional search context requires that consideration be given to the specialist, structured nature of the search strategies they employ. In this paper, we investigate a variety of query expansion methods applied to a collection of Boolean search strategies used in a variety of real-world professional search tasks. The results demonstrate the utility of context-free distributional language models and the value of using linguistic cues to optimise the balance between precision and recall.

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Section: Search Results Snippetsmentioning

confidence: 99%

Interactive query expansion for professional search applications

Russell-Rose

Gooch²,

Kruschwitz

2021

Business Information Review

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“…Even the fuzzy-based techniques are employed to deal with the uncertainty and ambiguity of the decision making process, the objectivity is limited due to the lack of enough evaluation data. To overcome the ambiguity that short queries cannot express the actual idea, Song et al (2014) developed a novel query recommendation technology using a hybrid semantic similarity measurement approach.…”

Section: Vehicle Product Recommendationmentioning

confidence: 99%

What attracts vehicle consumers’ buying

Zhou

Lim

et al. 2019

IMDS

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Purpose The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.

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“…Examples of machine learning and deep learning approaches include random forests, gradient boosting, convolutional neural networks, and pattern recognition. Supervised classification is one of the most popular pattern recognition approaches [32,119], which has been widely studied and applied to many domains, such as bioinformatics [13,84,203], human activity recognition [94,120,146,190], rare event forecasting [34,78,162], information retrieval [18,30,171,191], face recognition [9,134,135], fingerprint identification [79,143], Internet of Things [8,202], and more recently COVID-19 (also referred to as novel Coronavirus, 2019-nCOV and SARS-CoV-2) [138,182].…”

Section: Introductionmentioning

confidence: 99%

A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and Challenges

2020

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Supervised classification based on Contrast Patterns (CP) is a trending topic in the pattern recognition literature, partly because it contains an important family of both understandable and accurate classifiers. In this paper, we survey 105 articles and provide an in-depth review of CP-based supervised classification and its applications. Based on our review, we present a taxonomy of the existing application domains of CP-based supervised classification, and a scientometric study. We also discuss potential future research opportunities.

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An effective query recommendation approach using semantic strategies for intelligent information retrieval

Cited by 16 publications

References 37 publications

Interactive query expansion for professional search applications

Interactive query expansion for professional search applications

What attracts vehicle consumers’ buying

A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and Challenges

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