A collaborative filtering recommendation engine in a distributed environment

Ghuli, Poonam; Ghosh, Arko; Shettar, Rajashree

doi:10.1109/ic3i.2014.7019592

Cited by 15 publications

(5 citation statements)

References 13 publications

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“…The first step is to calculate the average rating of each movie as shown in equation (5). The amount of rating ( ) in the movie is divided by the COUNT rating value in the movie ( ).…”

Section: Collaborative Filteringmentioning

confidence: 99%

“…In the scale-out approach, an additional computer node is used to run a recommendation system to obtain good scalability. The scale-out method implemented in previous research was using MapReduce Hadoop as practiced by [4], [5], [6], and [7] to get good scalability from traditional collaborative filtering recommendation systems. Another study was conducted by [8] who used Apache Spark to implement a scale-out approach to overcome the scalability of the recommendation system with traditional collaborative filtering methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parallelization of Hybrid Content Based and Collaborative Filtering Method in Recommendation System with Apache Spark

Ikhsanudin

Winarko²

2019

Indonesian J. Comput. Cybern. Syst.

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Collaborative Filtering as a popular method that used for recommendation system. Improvisation is done in purpose of improving the accuracy of the recommendation. A way to do this is to combine with content based method. But the hybrid method has a lack in terms of scalability. The main aim of this research is to solve problem that faced by recommendation system with hybrid collaborative filtering and content based method by applying parallelization on the Apache Spark platform.Based on the test results, the value of hybrid collaborative filtering method and content based on Apache Spark cluster with 2 node worker is 1,003 which then increased to 2,913 on cluster having 4 node worker. The speedup got more increased to 5,85 on the cluster that containing 7 node worker.

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“…The first step is to calculate the average rating of each movie as shown in equation (5). The amount of rating ( ) in the movie is divided by the COUNT rating value in the movie ( ).…”

Section: Collaborative Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parallelization of Hybrid Content Based and Collaborative Filtering Method in Recommendation System with Apache Spark

Ikhsanudin

Winarko²

2019

Indonesian J. Comput. Cybern. Syst.

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“…Cold start user problem can be solved using this approach to some extent but cold start product problem cannot be solved. In [16] authors compared both the approach product based and user-based collaborative filtering and result showed that the execution time improves by 30% with every add-on of a node into the Hadoop cluster. And they conclude that product based approach has more scalability than the user based approach.…”

Section: Related Workmentioning

confidence: 99%

Hike the Performance of Collaborative Filtering Algorithm with the Inclusion of Multiple Attributes

Wadhvani¹,

Chauhan²

2018

IJITCS

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Abstract-At a recent time, digital data increases very speedily from small business to large business. In this span of internet explosion, choices are also increases and it makes the selection of products very difficult for users so it demands some recommendation system which provides good and meaningful suggestions to users to help them to purchase or select products of their own choice and get benefited. Collaborative filtering technique works very productive to provide personalized suggestions. It works based on the past given ratings, behavior and choices of users to provide recommendations. To boost its performance many other algorithms and techniques can be combined with it. This paper describes the method to boost the performance of collaborative filtering algorithm by taking multiple attributes in consideration where each attribute has some weight.

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“…That is , if user is new to the system then collaborative filtering is not suitable so we will apply popularity estimation to that user [1]. In paper [2], collaborative filtering method in distributed environment is explained in detail. In collaborative filtering by using rating history of the user similar item to the item rated by user is found out.…”

Section: IImentioning

confidence: 99%

Distributed Hybrid Book Recommendation System

Gandhi¹

2017

IJRASET

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Many e-commerce Websites are using recommendation systems to provide relevant suggestions to their customers. Every situation can't be solved by same solution. Therefore , in this paper we are providing distributed hybrid recommendation framework . User is divided into different types and according to user's type different algorithms are applied. In this paper solution for cold start problem is also provided. Aim of the paper is to improve efficiency of the recommendation system using dynamic task assignment and spark.

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A collaborative filtering recommendation engine in a distributed environment

Cited by 15 publications

References 13 publications

Parallelization of Hybrid Content Based and Collaborative Filtering Method in Recommendation System with Apache Spark

Parallelization of Hybrid Content Based and Collaborative Filtering Method in Recommendation System with Apache Spark

Hike the Performance of Collaborative Filtering Algorithm with the Inclusion of Multiple Attributes

Distributed Hybrid Book Recommendation System

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