Learning to rank experts using combination of multiple features of expertise

Kavitha, V.; Manju, G.; Geetha, T. V.

doi:10.1109/icacci.2014.6968216

Cited by 5 publications

(4 citation statements)

References 3 publications

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“…The count does not infer any information about the publication year. In order to give significance to the recent publications of the researcher, Kavitha et al (2014) introduced a novel measure, Time-weighted citation index:…”

Section: Time-weighted Citation Indexmentioning

confidence: 99%

“…We evaluated our approach using NDCG (Hang et al2011), to illustrate the significance of feature selection with two scenarios. NDCG gives importance to the top k retrieved entities and considers how these k entities are ordered (Kavitha et al, 2014). In the first scenario, we ignored the feature selection and considered all the features to compute four truncation levels of NDCG.…”

Section: Significance Of Feature Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Influential Researcher Identification in Academic Network Using Rough Set Based Selection of Time-Weighted Academic and Social Network Features

Manju

Kavitha

Geetha

2017

International Journal of Intelligent Information Technologies

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Researchers entering into a new research area are interested in knowing the current research trends, popular publications and influential (popular) researchers in that area in order to initiate their research. In this work, we attempt to determine the influential researcher for a specific topic. The active participation of the researchers in both the academic and social network activities signifies the researchers' influence level across time. The content and frequency of social interaction to a researcher reflects his or her influence. In our system, appropriate time-based social and academic features are selected using entropy based feature selection approach of rough set theory. A three layer model comprising semantically related concepts, researcher and social relations is developed based on the appropriate (influential) features. The researchers' topic trajectories are identified and recommended using Spreading activation algorithm. To cope up with the scalable academic network, map reduce paradigm has been employed in the spreading activation algorithm.

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Section: Time-weighted Citation Indexmentioning

confidence: 99%

Section: Significance Of Feature Selectionmentioning

confidence: 99%

Influential Researcher Identification in Academic Network Using Rough Set Based Selection of Time-Weighted Academic and Social Network Features

Manju

Kavitha

Geetha

2017

International Journal of Intelligent Information Technologies

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“…A. Kardan et al [15] proposed a model for expert finding on social networks where people in social networks are represented instead of web pages in PageRank. Recent works have concentrated on learning to rank models; V. Kavitha et al [16] combined multiple features of research expertise to rank experts; time weighted citation graph by giving significance to recent publications of an author and modified LDA to cope up with newly generated publication terms; Z. Yang et al [17] used a supervised learning model with features including language models, author-conference-topic model, and other ones. C. Moreira et al [18] proposed some features; academic indexes, regarding the textual content, Okapi BM25, TF and IDF were suggested in addition to some profile information; Sorg et al [19] proposed a discriminative model that allows the combination of different sources of evidence in a single retrieval model using Multi-Layer Perceptron (MLP) and Logistic Regression as regression classifiers.…”

Section: Related Workmentioning

confidence: 99%

Towards a Multi-Feature Enabled Approach for Optimized Expert Seeking

Abdullah¹,

Noureddine²,

Makki³

et al. 2017

Computer Science &Amp; Information Technology (CS &Amp; IT)

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With the enormous growth of data, retrieving information from the Web became more desirable and even more challenging because of the Big Data issues (e.g. noise, corruption, bad quality…etc.). Expert seeking, defined as returning a ranked list of expert researchers given a topic, has been a real concern in the last 15 years. This kind of task comes in handy when building scientific committees, requiring to identify the scholars' experience to assign them the most suitable roles in addition to other factors as well. Due to the fact the Web is drowning with plenty of data, this opens up the opportunity to collect different kinds of expertise evidence. In this paper, we propose an expert seeking approach with specifying the most desirable features (i.e. criteria on which researcher's evaluation is done) along with their estimation techniques. We utilized some machine learning techniques in our system and we aim at verifying the effectiveness of incorporating influential features that go beyond publications.

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“…Expertise can also be measured in extrinsic ways, through the judgement of peers. This can be gathered either directly through interviews or indirectly through citations (Kavitha et al, 2014). Related works in mining expertise in social networks are as follow.…”

Section: Introductionmentioning

confidence: 99%

Domain expertise extraction for finding rising stars

2022

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The field of expertise extraction utilizes published research enabling communities to highlight and identify the skills of researchers within specific scientific domains. This can be useful for evaluating research performance, and in the case of rising stars, in identifying top scientific talent. Previous research has harvested a range of publication indicators in an effort to identify expertise and talent. These include content indicators, citation metrics, and also the position of a researcher within a full collaboration network of scientists. The existing mechanism of expertise extraction utilizes all papers attributed to a scientific author, thereby potentially neglecting their specific or specialized expertise. Here we show that a tensor decomposition technique when applied to the problem addresses a number of useful problems. This includes better identification of individual expertise, as well as an integrated appraisal of an author’s role in an extended scientific network. The technique will afford new analyses of knowledge production which consider specialisation and diversity as core elements for further analysis. More generally the tensor decomposition techniques presented in this paper can be applied to a range of scientometric problems where multi-modal data is encountered.

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Learning to rank experts using combination of multiple features of expertise

Cited by 5 publications

References 3 publications

Influential Researcher Identification in Academic Network Using Rough Set Based Selection of Time-Weighted Academic and Social Network Features

Influential Researcher Identification in Academic Network Using Rough Set Based Selection of Time-Weighted Academic and Social Network Features

Towards a Multi-Feature Enabled Approach for Optimized Expert Seeking

Domain expertise extraction for finding rising stars

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