Product Recommendation using Multiple Filtering Mechanisms on Apache Spark

Authors

  • S.N. Patil Dept. Computer Engineering, Sandip Institute of Technology and Research Centre, Nashik, India
  • S.M. Deshpande Dept. Computer Engineering, Sandip Institute of Technology and Research Centre, Nashik, India
  • Amol D. Potgantwar Dept. Computer Engineering, Sandip Institute of Technology and Research Centre, Nashik, India

Keywords:

Bigdata, Collaborative, Hadoop

Abstract

Recommendation system is used by enormous users. Recommendations based on rating prediction provides useful recommendations for products, ratings are predicted using various methods. It is used commonly in recent years, and is used in variable areas in many popular applications which comprise of movies, songs, bulletin, files, research courses, online shopping, social networking sites, and product recommendation. Gradually, the amount of users, objects and facts has matured rapidly, the big data scrutiny problem for examination of recommender systems. Conventional recommender systems frequently suffer from scalability, efficiency and real time recommendation problems while processing or analyzing documents taking place at huge scale. To get rid from these problems, recommendation algorithms such as SVD, Trust SVD, and Incremental SVD are implemented on Apache Hadoop and Spark for evaluating most efficient recommendation. Analysis proves which prediction algorithm works more efficient than other algorithms. Proposed framework will provide real time and multiple recommendations to multiple users simultaneously.

 

References

Guibing Guo, Jie Zhang, and Neil Yorke-Smith, “A Novel Recommendation Model Regularized with User Trust and Item Ratings”, IEEE transactions on knowledge and data engineering, Vol.28, Issue.7, pp.1607-20, 2016.

Gejianxin and Liu jiaomin, “Software Test Cases Recommendation System Research based on Collaborative Filtering”, IEEESNPD, China, pp. 557-562, 2016.

Bartosz Kupisz and Olgierd Unold, “Collaborative Filtering Recommendation Algorithm based on Hadoop and Spark”, 2015 IEEE International Conference on Industrial Technology (ICIT), Spain, 1510-1514, 2015.

Anshu Choudhary and C.S. Satsangi, "Query Execution Performance Analysis of Big Data Using Hive and Pig of Hadoop", International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.91-97, 2015.

Brijesh Ahirwar and Vishal Pasi, "A Survey Paper on Inter-carrier Interference Reduction Techniques in OFDM Systems", International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.26-29, 2015.

Zhiyang Jia,Wei Gao, Yuting Yang, Xu Chen, “User-based Collaborative Filtering for Tourist Attraction Recommendations”, IEEE 2015International Conference on Computational Intelligence Communication Technology, Usa, PP.7-13, 2015.

Jyoti Gupta, Jayant Gadge, “Performance Analysis of Recommendation System Based On Collaborative Filtering and Demographics”, IEEE International Conference on Communication, Information Computing Technology (ICCICT), Indian, 1-6, 2015.

C. Thangamalar, P. Sathiyapriya, "Context-Aware Quantity Established Entrance Device Developing Operator Connection", International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.113-118, 2015.

V. Jain, "Outlier Detection Based on Clustering Over Sensed Data Using Hadoop", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.2, pp.45-50, 2013.

R. Murugesh, I. Meenatchi, "A Study Using PI on: Sorting Structured Big Data In Distributed Environment Using Apache Hadoop MapReduce", International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.35-38, 2014.

Kunhui Lin, JingjinWang, MeihongWang, “A Hybrid Recommendation Algorithm Based on Hadoop”, the 9th International Conference on Computer Science Education, Canada, pp. 540-543, 2014.

Yingya Zhang, Cheng Yang, ZhixiangNiu, “A Research of Job Recommendation System Based on Collaborative Filtering”, Seventh International Symposium on Computational Intelligence and Design, China, pp. 533-538, 2014.

Poonam Ghuli, Atanu Ghosh and Dr. Rajashree Shettar, “A Collaborative Filtering Recommendation Engine in a Distributed Environment”, India, pp.568-574,2014.

Xiao Peng, Shao Liangshan, Li Xiuran, “Improved collaborative filtering algorithm in the research and application of personalized movie recommendations”, Fourth International Conference on Intelligent Systems Design and Engineering Applications, China, pp. 349-352, 2013.

Suyun Wei, Ning Ye, Shuo Zhang, Xia Huang, Jian Zhu, “Item-based Collaborative Filtering Recommendation Algorithm Combining Item Category with Interestingness Measure”, International Conference on Computer Science and Service System, China, pp. 2038-2041, 2012.

Michael D. Ekstrand, John T. Riedl and Joseph A. Konstan, “Collaborative Filtering Recommender Systems”, Foundations and Trends R in Human Computer Interaction, Vol.4, No.2, pp. 291-324, 2010.

Wu Yueping and ZhengJianguo, “A research of recommendation algorithm based on cloud model”, China, pp. 469-473, 2010.

Yanhong Guo,Xuefen Cheng, Dahai Dong, Chunyu Luo and Rishuang Wang, “An improved collaborative filtering algorithm based on trust in e-commerce recommendation system”, Cina, pp.1-4, 2010.

Jun Wang, Arjen P. de Vries, “Unifying User based and Item based Collaborative Filtering Approaches by Similarity Fusion”, SIGIR06, USA, pp. 501-508, 2006.

Vlabo Kostov, Eiichi Naito, Jun Ozawa, “Cellular Phone Ringing Tone Recommendation System Based on Collaborative Filtering Method”, Proceeding 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, Japan, pp. 378-383,2003.

Simon Fong, Yvonne Ho, Yang Hang, “Using Genetic Algorithm for Hybrid Modes of Collaborative Filtering in Online Recommenders”, Eighth International Conference on Hybrid Intelligent Systems, Spain, pp. 174-179, 2008.

Zan Huang, Daniel Zeng, Hsinchun Chen, “A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce”, IEEE Intelligent Systems, Vol.22, Issue.5, pp. 68-78, 2007.

Xiangwei Mu, Yan Chen, Jinsong Zhang, “Improvement of Collaborative Filtering Algorithm Based on Hesitation Degree”, International Conference on Computer, Mechatronics, Control and Electronic Engineering , China, pp. 302-305, 2010.

Rahul R. Ghuleand Sachin N. Deshmukh, "Comparative Study on Speculative Execution Strategy to Improve MapReduce Performance", International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.197-200, 2015.

Dheeraj kumar Bokde, Sheetal Girase, Debajyoti Mukhopadhyay, “An Item-Based Collaborative Filtering using Dimensionality Reduction Techniques on Mahout Framework”, 2015 IEEE International Symposium on Nanoelectronic and Information Systems, India, 2015, pp. 231-236, 2003.

IJSRNSC Vol-5, Issue-3 June 2017

Downloads

Published

2017-06-30

How to Cite

[1]
S. Patil, S. Deshpande, and A. D. Potgantwar, “Product Recommendation using Multiple Filtering Mechanisms on Apache Spark”, Int. J. Sci. Res. Net. Sec. Comm., vol. 5, no. 3, pp. 76–83, Jun. 2017.

Issue

Vol. 5 No. 3 (2017): June

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.