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Design and Implementation of Collaborative Filtering Approach for Movie Recommendation System

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Anshu Sang, Santosh K. Vishwakarma
10.5120/ijca2017914490

Anshu Sang and Santosh K Vishwakarma. Design and Implementation of Collaborative Filtering Approach for Movie Recommendation System. International Journal of Computer Applications 167(12):18-24, June 2017. BibTeX

@article{10.5120/ijca2017914490,
	author = {Anshu Sang and Santosh K. Vishwakarma},
	title = {Design and Implementation of Collaborative Filtering Approach for Movie Recommendation System},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {167},
	number = {12},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {18-24},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume167/number12/27822-2017914490},
	doi = {10.5120/ijca2017914490},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Recommendation systems play a significant role in the user life that provides information filtering from enormous data to the user specific data for decision making. Recommendation system mainly deals with the similarity among objects (items). The collaborative filtering is a recommendation technique that contains a list of rating that the previous user has already given for an item. Using the rating and similarity among the two users, the system recommends an item to the user for the decision making. In the system, separate the movies data set into unrated and rated sample set. Then rated movies dataset is treated as training data with the help of “Item K-NN”, builds a model. The model is applied to the unrated movies dataset which is treated as testing data set. At last the order list of unrated movies is created on the basis of distance Pearson and Cosine formula.

References

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Keywords

Collaborative filtering, Recommender system, Item K-NN (Item recommendation), Item K-NN (Item Rating Prediction)