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VALIDATING EFFECTIVE RESUME BASED ON EMPLOYER’S INTEREST WITH RECOMMENDATION SYSTEM

Sivaramakrishnan, N, Subramaniyaswamy, V, Arunkumar, S and Soundaryarathna, P VALIDATING EFFECTIVE RESUME BASED ON EMPLOYER’S INTEREST WITH RECOMMENDATION SYSTEM. International Journal of Pure and Applied Mathematics, 2018, vol. 119, n. 12, pp. 13261-13272. [Journal article (Paginated)]

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English abstract

In current technological world, recruitment process of corporate has evolved to the greater extent. Both the candidates and the recruiters prefer resumes to be submitted as an e-document. Validating those resumes manually is not much flexible and effective and time saving. The team requires more man power to scrutinize the resumes of the candidates. The aim of our work is to help the recruiters to find the most appropriate resume that match all their requirements. The system allows the recruiter to post his/her requirement as query, and the system will recommend the relevant resume by calculating the similarity between the query and the resume using Vector Space Model (VSM).

Item type:	Journal article (Paginated)
Keywords:	recommender system, vector space model, term frequency, content based filtering, collaborative filtering
Subjects:	B. Information use and sociology of information B. Information use and sociology of information > BC. Information in society.
Depositing user:	Raster Daster
Date deposited:	02 Aug 2018 07:34
Last modified:	02 Aug 2018 07:34
URI:	http://hdl.handle.net/10760/33268

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References

[1] Badr HSSINA1, Abdelkrim MERBOUHA2, and Belaid BOUIKHALENE1, A Hybrid Approach of Semantic Similarity Calculation for a Content-based Recommendation of Text Documents on an E-learning Platform,(2016).

[2] Harry Zisopoulos, Content-Based Recommendation Systems (2014).

[3] Jiaze Chen, Liangcai Gao, Zhi Tang; Institute of Computer Science and Technology of Peking University; Beijing, P.R. China, Information Extraction from Resume Documents in PDF Format, (2016).

[4] M. IKONOMAKIS, S. KOTSIANTIS, V. TAMPAKAS, Text Classification Using Machine Learning Techniques. WSEAS TRANSACTIONS on COMPUTERS, Issue 8, Volume 4, August 2005, pp. 966-974.

[5] Ramos, J. Using tf-idf to determine word relevance in document queries. In Proceedings of the first instructional conference on machine learning, (2003, December).

[6] Akram Roshdi1 and Akram Roohparvar2, Review: Information Retrieval Techniques and Applications. International Journal of Computer Networks and Communications Security VOL. 3, NO. 9, SEPTEMBER 2015, 373–377.

[7] Michael J. Pazzani1and Daniel Billsus2 Content-based Recommendation Systems.

[8] Sebastiani F., “Machine Learning in Automated Text Categorization”, ACM Computing Surveys, vol. 34 (1), 2002, pp. 1-47.

[9] Jelena Jovanovic, Introduction to text mining.

[10] Rebecca A. Okaka, Waweru Mwangi, George Okeyo. A Hybrid Approach for Personalized Recommender System Using Weighted TFIDF on RSS Contents. International Journal of Computer Applications Technology and Research Volume 5–Issue 12, 764-774, 2016, ISSN:-2319–8656.

[11] Khan, Tabrez Juneja, Afzal Ayub Zubeda Momin, Adnan Ayyas Shaheen Gunduka, Rakesh Narsayya Godavari Sayed, ZainulAbideen Mohd Sadiq Naseem, Resume Ranking using NLP and Machine Learning.

[12] Marko B., Yoav S. Fab: Content-based, collaborative recommendation. Communications of the Association for Computing Machinery, 1997, 40(3):66–72.

[13] Mehdi Allahyari, Seyedamin Pouriyeh, Saied Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys Kochut. A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques.

[14] Gerard Salton and Chistopher Buckley, Term weighting approaches in automatic text retrieval. Information Processing & Management Vol. 24, No. 5, pp. 513-523, 1988.

[15] Juan Ramos, Using TF-IDF to Determine Word Relevance in Document Queries.

[16] Jaime Arguello INLS 509: Information Retrieval Vector space model, (2013).

[17] Tulasi K. Paradarami, Nathaniel D. Bastian, Jennifer L. Wightman, A hybrid recommender system using artificial neural networks, Expert Systems With Applications 83 (2017) 300–313

[18] Kun Yu, Gang Guan, Ming Zhou, Resume Information Extraction with Cascaded Hybrid Model

[19] Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., Gao, X. Z., & Indragandhi, V. (2017). A hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart city. Future Generation Computer Systems, 83, 653-673.

[20] Subramaniyaswamy, V., & Logesh, R. (2017). Adaptive KNN based Recommender System through Mining of User Preferences. Wireless Personal Communications, 97(2), 2229-2247.

[21] Logesh, R., & Subramaniyaswamy, V. (2017). A Reliable Point of Interest Recommendation based on Trust Relevancy between Users. Wireless Personal Communications, 97(2), 2751-2780.

[22] Logesh, R., & Subramaniyaswamy, V. (2017). Learning Recency and Inferring Associations in Location Based Social Network for Emotion Induced Point-of-Interest Recommendation. Journal of Information Science & Engineering, 33(6), 1629–1647.

[23] Subramaniyaswamy, V., Logesh, R., Abejith, M., Umasankar, S., & Umamakeswari, A. (2017). Sentiment Analysis of Tweets for Estimating Criticality and Security of Events. Journal of Organizational and End User Computing (JOEUC), 29(4), 51-71.

[24] Indragandhi, V., Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., Siarry, P., & Uden, L. (2018). Multi-objective optimization and energy management in renewable based AC/DC microgrid. Computers & Electrical Engineering.

[25] Subramaniyaswamy, V., Manogaran, G., Logesh, R., Vijayakumar, V., Chilamkurti, N., Malathi, D., & Senthilselvan, N. (2018). An ontology-driven personalized food recommendation in IoT-based healthcare system. The Journal of Supercomputing, 1-33.

[26] Arunkumar, S., Subramaniyaswamy, V., & Logesh, R. (2018). Hybrid Transform based Adaptive Steganography Scheme using Support Vector Machine for Cloud Storage. Cluster Computing.

[27] Indragandhi, V., Subramaniyaswamy, V., & Logesh, R. (2017). Resources, configurations, and soft computing techniques for power management and control of PV/wind hybrid system. Renewable and Sustainable Energy Reviews, 69, 129-143.

[28] Ravi, L., & Vairavasundaram, S. (2016). A collaborative location based travel recommendation system through enhanced rating prediction for the group of users. Computational intelligence and neuroscience, 2016, Article ID: 1291358.

[29] Logesh, R., Subramaniyaswamy, V., Malathi, D., Senthilselvan, N., Sasikumar, A., & Saravanan, P. (2017). Dynamic particle swarm optimization for personalized recommender system based on electroencephalography feedback. Biomedical Research, 28(13), 5646-5650.

[30] Arunkumar, S., Subramaniyaswamy, V., Karthikeyan, B., Saravanan, P., & Logesh, R. (2018). Meta-data based secret image sharing application for different sized biomedical images. Biomedical Research,29.

[31] Vairavasundaram, S., Varadharajan, V., Vairavasundaram, I., & Ravi, L. (2015). Data mining‐based tag recommendation system: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(3), 87-112.

[32] Logesh, R., Subramaniyaswamy, V., & Vijayakumar, V. (2018). A personalised travel recommender system utilising social network profile and accurate GPS data. Electronic Government, an International Journal, 14(1), 90-113.

[33] Vijayakumar, V., Subramaniyaswamy, V., Logesh, R., & Sivapathi, A. (2018). Effective Knowledge Based Recommeder System for Tailored Multiple Point of Interest Recommendation. International Journal of Web Portals.

[34] Subramaniyaswamy, V., Logesh, R., & Indragandhi, V. (2018). Intelligent sports commentary recommendation system for individual cricket players. International Journal of Advanced Intelligence Paradigms, 10(1-2), 103-117.

[35] Indragandhi, V., Subramaniyaswamy, V., & Logesh, R. (2017). Topological review and analysis of DC-DC boost converters. Journal of Engineering Science and Technology, 12 (6), 1541–1567.

[36] Saravanan, P., Arunkumar, S., Subramaniyaswamy, V., & Logesh, R. (2017). Enhanced web caching using bloom filter for local area networks. International Journal of Mechanical Engineering and Technology, 8(8), 211-217.

[37] Arunkumar, S., Subramaniyaswamy, V., Devika, R., & Logesh, R. (2017). Generating visually meaningful encrypted image using image splitting technique. International Journal of Mechanical Engineering and Technology, 8(8), 361–368.

[38] Subramaniyaswamy, V., Logesh, R., Chandrashekhar, M., Challa, A., & Vijayakumar, V. (2017). A personalised movie recommendation system based on collaborative filtering. International Journal of High Performance Computing and Networking, 10(1-2), 54-63.

[39] Senthilselvan, N., Udaya Sree, N., Medini, T., Subhakari Mounika, G., Subramaniyaswamy, V., Sivaramakrishnan, N., & Logesh, R. (2017). Keyword-aware recommender system based on user demographic attributes. International Journal of Mechanical Engineering and Technology, 8(8), 1466-1476.

[40] Subramaniyaswamy, V., Logesh, R., Vijayakumar, V., & Indragandhi, V. (2015). Automated Message Filtering System in Online Social Network. Procedia Computer Science, 50, 466-475.

[41] Subramaniyaswamy, V., Vijayakumar, V., Logesh, R., & Indragandhi, V. (2015). Unstructured data analysis on big data using map reduce. Procedia Computer Science, 50, 456-465.

[42] Subramaniyaswamy, V., Vijayakumar, V., Logesh, R., & Indragandhi, V. (2015). Intelligent travel recommendation system by mining attributes from community contributed photos. Procedia Computer Science, 50, 447-455.

[43] Vairavasundaram, S., & Logesh, R. (2017). Applying Semantic Relations for Automatic Topic Ontology Construction. Developments and Trends in Intelligent Technologies and Smart Systems, 48.

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