A frequent keyword-set based algorithm for topic modeling and clustering of research papers

Shubankar, Kumar; Singh, AdityaPratap; Pudi, Vikram

doi:10.1109/dmo.2011.5976511

Cited by 18 publications

(22 citation statements)

References 23 publications

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“…Proposed [5] a method that uses closed frequent keyword-set of the titles' phrases to form topics. In their proposed approach, they form closed frequent keyword-sets by top-down dissociation of keywords from the phrases present in the paper's titles on a user-defined minimum support.…”

Section: Related Workmentioning

confidence: 99%

A paper recommender system based on user’s profile in big data scholarly

Aghamirzad

Honarvar

Jokar

2016

J. Fundam and Appl Sci.

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Users encounter a huge volume of papers in digital libraries and paper search engines such as IEEE Explore, ACM Digital library, Google scholar and etc. these high number of papers make some difficulties for researchers for finding proper information and items.Recommender systems contain successful tools for knowledge of users and identification of their priorities. These systems present a personalized proposal to users who seek to find a special kind of relevant data or their priorities through the big number of data.Recommendersystem based on personalization uses the user profile and in view of the fact that the user profile encompass information pertaining to the user priorities; so it is a very active district in data recovery. Recommendersystem is an attitude that presented in order to encounter difficulties caused by abundant data and it helps users to attain their goals quickly through huge number of data. In this paper, we have presented an approach that received entire of available information in a paper, and formed a profile for each user by short and long inquiries; this profile is personalized for user and then recommends the closest paperto the Recommender systems contain successful tools for knowledge of users and identification of their priorities. These systems present a personalized proposal to users who seek to find a special kind of relevant data or their priorities through the big number of data.Recommendersystem based on personalization uses the user profile and in view of the fact that the user profile encompass information pertaining to the user priorities; so it is a very active district in data recovery. Recommendersystem is an attitude that presented in order to encounter difficulties caused by abundant data and it helps users to attain their goals quickly through huge number of data. In this paper, we have presented an approach that received entire of available information in a paper, and formed a profile for each user by short and long inquiries; this profile is personalized for user and then recommends the closest paperto the Recommender systems contain successful tools for knowledge of users and identification of their priorities. These systems present a personalized proposal to users who seek to find a special kind of relevant data or their priorities through the big number of data.Recommendersystem based on personalization uses the user profile and in view of the fact that the user profile encompass information pertaining to the user priorities; so it is a very active district in data recovery. Recommendersystem is an attitude that presented in order to encounter difficulties caused by abundant data and it helps users to attain their goals quickly through huge number of data. In this paper, we have presented an approach that received entire of available information in a paper, and formed a profile for each user by short and long inquiries; this profile is personalized for user and then recommends the closest paperto the J FundamAppl Sci. 2016, 8(2S), 942-955 942 user...

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Section: Related Workmentioning

confidence: 99%

A paper recommender system based on user’s profile in big data scholarly

Aghamirzad

Honarvar

Jokar

2016

J. Fundam and Appl Sci.

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“…Shubankar, et al, [8] defined a phrase P as a run of words between two stop-words in the title of a research paper. They used a comprehensive list of 671Standard English stopwords.…”

Section: Algorithm 1 Topics Extraction Based On Phrases In the Papermentioning

confidence: 99%

“…Shubankar, et al, [8] proposed a method that uses closed frequent keyword-set of the titles' phrases to form topics. In their proposed approach, they form closed frequent keyword-sets by top-down dissociation of keywords from the phrases present in the paper's titles on a user-defined minimum support [27].…”

Section: Paper's Topics Extractionmentioning

confidence: 99%

“…Shubankar, et al, [8] extracts closed frequent keyword-sets as topics after many steps and iterations. Firstly, phrases are formulated, from phrases, the method extracts keywordsets, from keyword-sets; frequent keywords-sets are formed and finally from frequent keywords-set, closed frequent keywords-sets are extracted as topics.…”

Section: They Are: a An Based On Based On Using For By Of Inmentioning

confidence: 99%

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An Effective Academic Research Papers Recommendation for Non-profiled Users

Hanyurwimfura¹,

Liu²,

Havyarimana³

et al. 2015

IJHIT

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“…In [37] the authors cluster them into hierarchical overlapping clusters using the topics discussed in them as a similarity measure. The authors ranked the research papers in topic clusters by using a modified PageRank algorithm.…”

mentioning

confidence: 99%

Prototype/topic based clustering method for weblogs

Perez-Tellez

Cardiff

Rosso

et al. 2016

IDA

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Abstract. In the last 10 years, the information generated on weblog sites has increased exponentially, resulting in a clear need for intelligent approaches to analyse and organise this massive amount of information. In this work, we present a methodology to cluster weblog posts according to the topics discussed therein, which we derive by text analysis. We have called the methodology Prototype/Topic Based Clustering, an approach which is based on a generative probabilistic model in conjunction with a Self-Term Expansion methodology. The usage of the Self-Term Expansion methodology is to improve the representation of the data and the generative probabilistic model is employed to identify relevant topics discussed in the weblogs. We have modified the generative probabilistic model in order to exploit predefined initialisations of the model and have performed our experiments in narrow and wide domain subsets. The results of our approach have demonstrated a considerable improvement over the pre-defined baseline and alternative state of the art approaches, achieving an improvement of up to 20% in many cases. The experiments were performed on both narrow and wide domain datasets, with the latter showing better improvement. However in both cases, our results outperformed the baseline and state of the art algorithms.

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A frequent keyword-set based algorithm for topic modeling and clustering of research papers

Cited by 18 publications

References 23 publications

A paper recommender system based on user’s profile in big data scholarly

A paper recommender system based on user’s profile in big data scholarly

An Effective Academic Research Papers Recommendation for Non-profiled Users

Prototype/topic based clustering method for weblogs

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