A Prospective Extension Through an Analysis of the Existing Movie Recommendation Systems and Their Challenges


KIPS Transactions on Computer and Communication Systems, Vol. 12, No. 1, pp. 25-40, Jan. 2023
https://doi.org/10.3745/KTCCS.2023.12.1.25,   PDF Download:
Keywords: Recommendation, Collaborative Filtering, Content-based Filtering, Artificial intelligence, Neural Network
Abstract

Recommendation systems are frequently used by users to generate intelligent automatic decisions. In the study of movie recommendation system, the existing approach uses largely collaboration and content-based filtering techniques. Collaborative filtering considers user similarity, while content-based filtering focuses on the activity of a single user. Also, mixed filtering approaches that combine collaborative filtering and content-based filtering are being used to compensate for each other's limitations. Recently, several AI-based similarity techniques have been used to find similarities between users to provide better recommendation services. This paper aims to provide the prospective expansion by deriving possible solutions through the analysis of various existing movie recommendation systems and their challenges.


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Cite this article
[IEEE Style]
C. N. Z. Latt, M. Firdaus, M. Aguilar, K. Rhee, "A Prospective Extension Through an Analysis of the Existing Movie Recommendation Systems and Their Challenges," KIPS Transactions on Computer and Communication Systems, vol. 12, no. 1, pp. 25-40, 2023. DOI: https://doi.org/10.3745/KTCCS.2023.12.1.25.

[ACM Style]
Cho Nwe Zin Latt, Muhammad Firdaus, Mariz Aguilar, and Kyung-Hyune Rhee. 2023. A Prospective Extension Through an Analysis of the Existing Movie Recommendation Systems and Their Challenges. KIPS Transactions on Computer and Communication Systems, 12, 1, (2023), 25-40. DOI: https://doi.org/10.3745/KTCCS.2023.12.1.25.