KNN based Entertainment Enhancing System

Gafoor, Abdul; Srujana, Athi Lakshmi; Nagasri, Arvapalli; Durgaprasad, Garikipati Sri Sai; Dasari, Lokesh Sai Kumar

doi:10.1109/icoei53556.2022.9777225

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…The study results show that the information display can be used to search for smartphone recommendation systems. Gafoor et al (2022) proposed a film recommendation system developed using one of the most powerful, well-known, and widely used KNN machine learning algorithms to improve the prediction of the likelihood of specific digital content to users whose likelihood was previously analyzed.…”

Section: Literature Reviewmentioning

confidence: 99%

Recommendation System Using the K-Nearest Neighbor Approach: A Case Study of Dual Camera Quality as a Smartphone Selection Criterion

Pasha,

Muﬂihah

2022

J. Inf. Technol. Cyber Secur.

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Many smartphones today need to be more precise about choosing one that suits the user's needs. In fact, smartphone sellers sometimes need help recommending smartphones that suit buyers' needs. Generally, buyers search for smartphone specifications with keywords they desire, but the results appear different from what they expected. Users need the main specifications, such as Random Access Memory (RAM) and Read Only Memory (ROM) capacity, battery, and high camera quality. This research aims to implement the K-Nearest Neighbor (KNN) algorithm for recommendation smartphone selection based on the criteria mentioned. The data test results show that the combination of KNN with four criteria has good performance, as indicated by the accuracy, precision, recall, and f-measure values of 95%, 94%, 97%, and 95%, respectively.

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Section: Literature Reviewmentioning

confidence: 99%