research-article

Parallel Ratio Based CF for Recommendation System

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Mosin HasanAuthors Info & Claims

ICCCNT '16: Proceedings of the 7th International Conference on Computing Communication and Networking Technologies

Article No.: 34, Pages 1 - 4

https://doi.org/10.1145/2967878.2967916

Published: 06 July 2016 Publication History

Abstract

With the increase in E-commerce, Recommendation Systems are getting popular to provide recommendations of various items (movies, books, music) to users. To build the Recommendation System (RS), Collaborative Filtering (CF) techniques are proven efficient. From the main two Collaborative Filtering techniques i.e. User-Based and Item-Based, survey suggest that Item-Based CF provides better recommendations. A novel approach, Ratio-Based CF provides recommendation depending upon the item's ratio is more accurate comparatively but face scalability problem. To overcome this problem a parallel approach can be used instead of sequential. Our experiments shows that Ratio Based CF techniques have more accuracy comparatively as well as Parallel (Hadoop) implementation of Ratio Based CF Techniques have drastically reduce the training time (i.e. ratio calculating time between each pair of items) from 90 minutes in Java to 5 minutes in Hadoop for sub-data of MovieLens 100K dataset.

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Cited By

Azzopardi J(2018)Item-Based Vs User-Based Collaborative Recommendation PredictionsSemantic Keyword-Based Search on Structured Data Sources10.1007/978-3-319-74497-1_16(165-170)Online publication date: 8-Feb-2018
https://doi.org/10.1007/978-3-319-74497-1_16
Mansur FPatel VPatel M(2017)A review on recommender systems2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)10.1109/ICIIECS.2017.8276182(1-6)Online publication date: Mar-2017
https://doi.org/10.1109/ICIIECS.2017.8276182

Parallel Ratio Based CF for Recommendation System
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

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ICCCNT '16: Proceedings of the 7th International Conference on Computing Communication and Networking Technologies

July 2016

262 pages

ISBN:9781450341790

DOI:10.1145/2967878

Copyright © 2016 ACM.

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University of North Texas: University of North Texas

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New York, NY, United States

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Published: 06 July 2016

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ICCCNT '16

ICCCNT '16: 7th International Conference on Computing Communication and Networking Technologies

July 6 - 8, 2016

TX, Dallas, USA

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ICCCNT '16 Paper Acceptance Rate 48 of 101 submissions, 48%;

Overall Acceptance Rate 48 of 101 submissions, 48%

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Cited By

Azzopardi J(2018)Item-Based Vs User-Based Collaborative Recommendation PredictionsSemantic Keyword-Based Search on Structured Data Sources10.1007/978-3-319-74497-1_16(165-170)Online publication date: 8-Feb-2018
https://doi.org/10.1007/978-3-319-74497-1_16
Mansur FPatel VPatel M(2017)A review on recommender systems2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)10.1109/ICIIECS.2017.8276182(1-6)Online publication date: Mar-2017
https://doi.org/10.1109/ICIIECS.2017.8276182

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