The Development of Movie Recommendation System with Graph Data Structure
DOI:
https://doi.org/10.14456/jiskku.2023.30Keywords:
Recommendation system, Graph data structure, Maximum spanning treeAbstract
Purpose: The objective of this research is to design and evaluate the efficiency a movie recommendation process with graph data structure.
Methodology: The MovieLens dataset contains 100,000 records on 1,682 movies from 943 users. There are two parts of the study 1) the recommendation based on movie preference ratings by K - mean clustering method and 2) the recommendation based on a spanning tree of maximum weights in graph data structure by user’s attributions.
Findings: The recommendations for top – 10 movies based on movie preference ratings from 5 user groups by K – mean Clustering. The result has shown that the average recommendation accuracy is 28.16%. In addition to the recommendation for top-10 movies based on graph data structure from 111 user groups by user’s attributions, such as sex, age rage, and occupation found that the average recommendation accuracy is 87.45%.
Applications of this study: The results indicated that the proposed maximum weight spanning tree in graph data structure can recommend movies to watching more efficiently.
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