Popularity Based Recommendation System
Keshetti Sreekala
Keshetti Sreekala*, Department of CSE, MGIT, Hyderabad, India.
Manuscript received on February 06, 2020. | Revised Manuscript received on February 10, 2020. | Manuscript published on February 30, 2020. | PP: 1561-1566 | Volume-9 Issue-3, February, 2020. | Retrieval Number: B4660129219/2020©BEIESP | DOI: 10.35940/ijeat.B4660.029320
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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 Recommendation engine recommends the most relevant items to the user by using different algorithms to filter the data. A Recommendation system is more useful in the context of data extraction relating to applications of big data and machine learning. As the name indicates Popularity based recommendation system works with the current vogue. It basically uses the items which are in swing at present. This is the most basic recommendation system which provides generalized recommendation to every user depending on the popularity. Whatever is more popular among the general public that is more likely to be recommended to new customers. The generalized recommendation not personalized is based on the count. In this paper I am going to use a class that we created which includes the methods to create recommendations and to recommend the item to the user. Next I will load the data of Comma Separated Value (CSV). After that sort the sound name based on the how many users have listened to the sound name. After the collection of data code splits the dataset into training and the test dataset using 80–20 ratio. This creates an instance of popularity based recommenders class. At last I will use the popularity model to make the predictions.
Keywords: Popularity, recommendation, sound name, prediction, listen, dataset, filter, trend.