Intelligent Collaborative Recommender System by Crow Search Algorithm and K-Means Algorithm
Eshetu Tesfaye1, Pooja2, Rani Astya3
1Eshetu Tesfaye, Student, Sharda University, Greater Noida,(U.P.), India.
2Pooja, Associate Professor, Department of Computer Science and Engineering, Sharda University, Greater Noida, (U.P.), India.
3Rani Astya, Department of Computer Science and Engineering, Sharda University, Greater Noida, (U.P.), India.
Manuscript received on 18 March 2019 | Revised Manuscript received on 24 March 2019 | Manuscript published on 30 July 2019 | PP: 4463-4471 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2950078219/19©BEIESP | DOI: 10.35940/ijrte.B2950.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A recommender framework is a data refining engines that seeks to foresee the rating for customers and things from enormous information to suggest their preferences. Movie suggestion frameworks give a system to help customers in arranging customers with practically identical interests. This causes a recommender framework basically a focal piece of sites and internet business application. In this study, we have developed a collaborative movie recommender system using crow search and K-means algorithm. This article centers on the movie suggestion proposal frameworks whose essential goal is to recommend a recommender framework through information bunching and computational insight. We have used Elbow method and Silhouette score to select right k number of clusters and calculate errors in each cluster respectively. We have used evaluation metrics standard deviation, mean absolute error, and root mean absolute error to evaluate the performance of the proposed system. The experiment result shows 0.635 MAE and 0.758 RMSE which indicates that our framework accomplished better execution contrast with other existing approaches.
Keywords: Collaborative Filtering, Crow Search Optimization, E-Commerce, K-means, Recommendation System
Scope of the Article: E-Commerce