Movie Recommendation System Based on Machine Learning
DOI:
https://doi.org/10.54097/hbem.v21i.14740Keywords:
Machine Learning, Movie Recommendation System.Abstract
In today’s era, with the growth of businesses on an individual basis, many applications are trying to provide more effective services to individual users, and eventually launched personalized services. Simply put, when a user enters certain keywords in a search engine, the recommendation system can push certain information or products to the user. Based on this principle, there are personalized recommendation videos on the homepage of short video platforms, and various music software have launched personalized radio stations. Even if you want to watch a movie on Netflix, it can recommend it to you. These applications are all looking for similar content in the content that users are interested in and recommending it. A simple movie recommendation system only matches similar movies through user history records, and the accuracy of matching is not high and easily affects efficiency. Therefore, a simple movie recommendation system can no longer meet user needs. The movie recommendation system that incorporates Spark and machine learning algorithms can better serve the public. This article will explore the impact of adding different algorithms to machine learning content on the matching degree of recommended content.
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Ying-jie ZENG. Design and Implementation of Movie Recommendation System Based on Spark and User Preference [D]. Zhejiang University of Technology, 2020.
Smitha, N, et al. “A Review on Movie Recommendation System Using Machine Learning.” IEEE Xplore, 1 Feb. 2021, ieeexplore.ieee.org/document/9388619.
MA Shuai. (2022). Research on improvement of movie recommendation algorithm based on user behavior. Electronic Design Engineering (19), 60-64. doi: 10.14022/j.issn1674-6236.2022.19.013.
Dean J, Ghemawat S. MapReduce: Simplified Data Processing on Large Clusters[C]//Proceedings of 6th Conference on Symposium on Operating Systems Design and Implementation. San Francisco: IEEE, 2004: 107-113.
YANG Dong-chen. Research and Design of Micro - Video Recommendation System Based on Spark Big Data [D]. Nanchang University, 2021.
Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I. Spark: Cluster computing with working sets. HotCloud, 2010, 10.
ZHANG Xiao-qian. Recommendation System Based on Machine Learning Algorithms [J]. CHINA SCIENCE AND TECHNOLOGY INFORMATION, 2023(11): 104-107.
XU Wen-ying, XIANG Qiang. Application and implementation of collaborative filtering recommendation algorithm based on Pyspark platform [J]. Journal 0f Southwest Minzu University (Natural Science Edition), 2018, 44(2): 202-207. DOI: 10.11920/xnmdzk.2018.02.013.
Hrisav Bhowmick, Ananda Chatterjee, Jaydip Sen. “Comprehensive Movie Recommendation System.” ArXiv.org, 23 Dec. 2021, arxiv.org/abs/2112.12463?context=cs.IR. Accessed 12 Aug. 2023.
YAN Shui-ge, FU Zhen-zhen. Research on Dining Windows Recommendation AIgorithm for Students in Higher Education Based on Mahout [J]. Modern Educational Technology Centre, Nantong University, Nantong 226000), 2015(8): 24-27, 34. DOI: 10.3969/j.issn.1007-1423.2015.23.005.
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