Whale Optimization Based Recommendation System
Bharti Sharma1, Adeel Hashmi2, Ankit Kumar3

1Bharti Sharma, Department of Computer Engineering, BMU Rohtak, Haryana, India.
2Adeel Shiraz Hashmi, Department of Computer Engineering, Jamia Millia Islamia, Delhi, India.
3Dr Ankit Kumar, Department of Computer Engineering, BMU university, Rohtak, Harayna, India.

Manuscript received on 05 August 2019 | Revised Manuscript received on 10 August 2019 | Manuscript published on 30 August 2019 | PP: 2640-2644 | Volume-8 Issue-10, August 2019 | Retrieval Number: J93730881019/19©BEIESP | DOI: 10.35940/ijitee.J9373.0881019
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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: Recommendation System is an information filtering system which seeks to predict the “liking” of a user for an item, with the aim to suggest the user those items which he/she is most likely to select/buy. The focus of this paper is on rating prediction whose main objective is to predict the ratings the current user is going to give to the items which are yet to be rated/viewed by him/her. This paper uses a collaborative filtering based approach for generating recommendation, and the model used is a clustering-based model. In this approach all the existing users are clustered using whale optimization technique, instead of traditional clustering approaches like k-means, EM algorithm, etc. The appropriate cluster is then identified for the active user, and the ratings of the active user are predicted based on ratings given by other users belonging to the same cluster. Different measures like MAE, SD, RMSE and t-value are used for performance analysis of the proposed method and the results obtained are found to be highly accurate.
Index Terms: Collaborative Filtering, MAE, SD, RMSE, T-Value, WCSS.

Scope of the Article: Discrete Optimization