Multi-Step Iterative Algorithm for Feature Selection on Dynamic Documents

Bafna, Prafulla B.; Shirwaikar, Shailaja; Pramod, Dhanya

doi:10.4018/ijirr.2016040102

Cited by 7 publications

(4 citation statements)

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“…Group Frequency of this synset group is computed based on the frequency of occurrence of different terms in the synset group. For example, if customer, client and consumer have frequency of occurrence 6, 3 and 2, respectively, then the frequency of the group is computed as (6 × 1) + (3 × 0.7) + (2 × 0.6) = 6 + 2.1 + 1.2 = 9 (Bafna et al , 2016).…”

Section: Methodsmentioning

confidence: 99%

“…Text mining techniques like clustering, feature extraction and so on can be beneficial. In text mining, major terms (frequently occurring) are considered as features representing a document (Bafna et al , 2016; Sun and Vasarhelyi, 2018). Clustering helps in grouping documents based on the similarity between the terms present in the document.…”

Section: Introductionmentioning

confidence: 99%

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Task recommender system using semantic clustering to identify the right personnel

Bafna¹,

Shirwaikar

Pramod

2019

VJIKMS

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Purpose Text mining is growing in importance proportionate to the growth of unstructured data and its applications are increasing day by day from knowledge management to social media analysis. Mapping skillset of a candidate and requirements of job profile is crucial for conducting new recruitment as well as for performing internal task allocation in the organization. The automation in the process of selecting the candidates is essential to avoid bias or subjectivity, which may occur while shuffling through thousands of resumes and other informative documents. The system takes skillset in the form of documents to build the semantic space and then takes appraisals or resumes as input and suggests the persons appropriate to complete a task or job position and employees needing additional training. The purpose of this study is to extend the term-document matrix and achieve refined clusters to produce an improved recommendation. The study also focuses on achieving consistency in cluster quality in spite of increasing size of data set, to solve scalability issues. Design/methodology/approach In this study, a synset-based document matrix construction method is proposed where semantically similar terms are grouped to reduce the dimension curse. An automated Task Recommendation System is proposed comprising synset-based feature extraction, iterative semantic clustering and mapping based on semantic similarity. Findings The first step in knowledge extraction from the unstructured textual data is converting it into structured form either as Term frequency–Inverse document frequency (TF-IDF) matrix or synset-based TF-IDF. Once in structured form, a range of mining algorithms from classification to clustering can be applied. The algorithm gives a better feature vector representation and improved cluster quality. The synset-based grouping and feature extraction for resume data optimizes the candidate selection process by reducing entropy and error and by improving precision and scalability. Research limitations/implications The productivity of any organization gets enhanced by assigning tasks to employees with a right set of skills. Efficient recruitment and task allocation can not only improve productivity but also cater to satisfy employee aspiration and identifying training requirements. Practical implications Industries can use the approach to support different processes related to human resource management such as promotions, recruitment and training and, thus, manage the talent pool. Social implications The task recommender system creates knowledge by following the steps of the knowledge management cycle and this methodology can be adopted in other similar knowledge management applications. Originality/value The efficacy of the proposed approach and its enhancement is validated by carrying out experiments on the benchmarked dataset of resumes. The results are compared with existing techniques and show refined clusters. That is Absolute error is reduced by 30 per cent, precision is increased by 20 per cent and dimensions are lowered by 60 per cent than existing technique. Also, the proposed approach solves issue of scalability by producing improved recommendation for 1,000 resumes with reduced entropy.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Task recommender system using semantic clustering to identify the right personnel

Bafna¹,

Shirwaikar

Pramod

2019

VJIKMS

Self Cite

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“…Synonyms and Meronyms are identified for each term and assembled into groups to form the synsets. The synsets having group frequency greater than the threshold frequency are chosen to represent the columns and reviews are placed in rows [20]. The synset document matrix containing the group frequency count is normalized to form the feature matrix.…”

Section: Review Based Feature Matrix Generationmentioning

confidence: 99%

Review based Feature Matrix for Predicting ratings in Recommender System

Bafna¹,

Shirwaikar²,

Pramod³

et al. 2017

IJET

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Abstract-Recommender systems are acquiring extensive popularity and have become essential component of on-line business handling tools because of their capability of providing personalized guidance in selecting products and services. Collaborative Filtering that brings in the popularity aspect of the item amongst the user base, heavily depends on the ratings provided by the users, while Contentbased Filtering that brings out the item's features matching the user's taste, requires content information as also user's preference information. Service providers usually invite users to share their experience about the use of service in the form of reviews and ratings. Reviews which are verbose contain a rich source of information about the service's features as also user's preferences while ratings are usually sparse due to user's reluctance to quantify. A feature matrix generated by processing review information using semantic similarity based on synsets can be used along with sparse ratings to generate the complete predicted ratings matrix. The paper presents a modified matrix factorization approach for recommendations using the review based feature matrix Keyword-Recommender system, synsets, predictive model. I. INTRODUCTION Recommender systems(RS) have become common place in the digital market arena, by providing the on line user of products and services, a personalized selection guidelines. Generally technology used by RS is categorized into two groups as Content-based and Collaborative [1]. Content based systems are based on user profile and item description, giving importance to various features of items that may interest the user or match his taste.It recommends the item that is having the features that user has liked in the previously consumed items [2].Collaborative Filtering harnesses on popularity element or standing of the item across theuser base and works by collecting user feedback in the form of ratings of items. The challenges faced by collaborative filtering are i. The sparsity of rating matrix due to reluctance on the part of user to rate all the items. ii.The unavailability of data for new user or new item which is also known as cold start problem. iii. Hijacking of the RS by pushing own product rating to the higher value and lowering the competitor's rating leading to reduction in the quality of recommendations. Matrix factorization is widely used, model based approach that estimates ratings. It is a class of latent factor models, where user and items are represented as unknown feature vectors along latent dimensions. The feature vectors are learned using known ratings and learnt features are then used to predict unknown ratings. In the presence of sparse ratings, reviews in textual form present a rich source of feature information. The reviews are converted into the Feature Matrix with group of terms based on synsets as a feature, utilizing Semantic relationship existing between the terms in the form of Synonyms and meronyms. In this paper Matrix factorization approach uses the above generated fe...

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“…Entropy is consistent even for large data size. Document Management System (DMS) facilitates to access the documents in a fast and easy way and turn increases the productivity of the work [52][53]. Grouping of documents is one of the most important steps towards document management, which helps in identifying replicas of documents, clustering search engine results, and so on [54][55].…”

Section: Introductionmentioning

confidence: 99%

Marathi Text Analysis using Unsupervised Learning and Word Cloud

Bafna¹,

Saini²

2020

IJEAT

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Managing a large number of textual documents is a critical and significant task and supports many applications ranging from information retrieval to clustering search engine results. Marathi is one of the oldest of the regional languages in the Indo-Aryan language family, dating from about AD 1000. Abundance of Marathi literature has generated a big corpus and need of summarization of information. The objective of this study is to overcome the scalability problem while managing the documents and summarize the Marathi corpus by extracting tokens. The work is better in terms of scalability and supports the consistent quality of cluster for incremental data set. Most of the past and contemporary research works have targeted English corpus document management. Marathi corpus has been mostly exploited by the researchers for exploring stemming, single-document summarization and classifier design on Marathi corpus. Implementing unsupervised learning on the Marathi corpus for summarization of multiple documents through Word Cloud is still an untouched area. Technically speaking, the current work is an application of TF-IDF, cosine-based document similarity measures and cluster dendrograms, in addition to various other Natural Language Processing (NLP) activities. Entropy and precision are used to evaluate the experiments carried on different datasets and results prove the robustness of the proposed approach for Marathi Corpus.

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Multi-Step Iterative Algorithm for Feature Selection on Dynamic Documents

Cited by 7 publications

References 30 publications

Task recommender system using semantic clustering to identify the right personnel

Task recommender system using semantic clustering to identify the right personnel

Review based Feature Matrix for Predicting ratings in Recommender System

Marathi Text Analysis using Unsupervised Learning and Word Cloud

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