协同过滤
推荐系统
奇异值分解
矩阵分解
电影
计算机科学
正规化(语言学)
因式分解
情报检索
人工智能
数据挖掘
算法
量子力学
物理
特征向量
作者
Avis Priyati,Arif Dwi Laksito,Heri Sismoro
标识
DOI:10.1109/icoiact55506.2022.9972018
摘要
The recommendation system has been a vital study topic in recent years, so many scientists and academics across the world are interested in researching the subject. Music, movies, books, news, commercial items, and search inquiries are all examples of applying the recommendation system. One of the most common and successful strategies in recommendation systems is collaborative filtering. This method seeks to find similar users who are active to recommend an item. The author proposes to compare model-based collaborative filtering techniques using matrix factorization algorithms, such as alternating least squares (ALS), singular value decomposition (SVD), Alternating Least Squares weight regularization (ALS-WR) and SVD++. Furthermore, the quality of the recommendation system is determined using three datasets with various features. According to the research findings of four algorithms (ALS, ALS-WR, SVD, SVD++), only the MovieLens dataset can overcome the sparsity problem, as seen by the RMSE score below 1.
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