基于项目最高相似度的协同过滤

M. Muhammad, S. Sugiyanto
{"title":"基于项目最高相似度的协同过滤","authors":"M. Muhammad, S. Sugiyanto","doi":"10.29099/ijair.v6i1.310","DOIUrl":null,"url":null,"abstract":"The popularity of movies has increased in recent years. There are thousands of films produced each year. These films make it challenging for movie lovers to pick the ideal film to see. We propose a recommendation system that strives to offer guidance in selecting films.  Depending on the method employed, recommendation systems can be categorized into three groups: collaborative filtering, content-based filtering, and hybrid filtering. In this work, collaborative filtering, one of the methods frequently used in recommendation systems was used. There are two ways to the Collaborative Filtering approach: User-Based Collaborative Filtering (UBCF) and Item-Based Collaborative Filtering (IBCF). There are two methods for finding similar items or users: Cosine and Pearson similarities. The Cosine similarity approach is one way to determine how similar two items are. Additionally, the Pearson Correlation Coefficient approach, which determines similarities between objects by calculating linear correlations between two sets, is the most widely employed. This study aims to determine which system produces the highest item similarity in IBCF and predicted ratings to actual ratings using 90% training and 10% testing data. The data set taken from MovieLens.org consists of 943 users from 1664 movies with 99392 ratings. The MovieLens data collection will be analyzed with the RStudio and the R package recommenderlab. The results reveal that the IBCF with Cosine similarities shows the number of items recommended n top-rated movies to each user for 10 movies. The IBCF can identify the most recommended films and creates a frequency distribution of items.","PeriodicalId":334856,"journal":{"name":"International Journal of Artificial Intelligence Research","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Item Based Collaborative Filtering Based on Highest Item Similarity\",\"authors\":\"M. Muhammad, S. Sugiyanto\",\"doi\":\"10.29099/ijair.v6i1.310\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The popularity of movies has increased in recent years. There are thousands of films produced each year. These films make it challenging for movie lovers to pick the ideal film to see. We propose a recommendation system that strives to offer guidance in selecting films.  Depending on the method employed, recommendation systems can be categorized into three groups: collaborative filtering, content-based filtering, and hybrid filtering. In this work, collaborative filtering, one of the methods frequently used in recommendation systems was used. There are two ways to the Collaborative Filtering approach: User-Based Collaborative Filtering (UBCF) and Item-Based Collaborative Filtering (IBCF). There are two methods for finding similar items or users: Cosine and Pearson similarities. The Cosine similarity approach is one way to determine how similar two items are. Additionally, the Pearson Correlation Coefficient approach, which determines similarities between objects by calculating linear correlations between two sets, is the most widely employed. This study aims to determine which system produces the highest item similarity in IBCF and predicted ratings to actual ratings using 90% training and 10% testing data. The data set taken from MovieLens.org consists of 943 users from 1664 movies with 99392 ratings. The MovieLens data collection will be analyzed with the RStudio and the R package recommenderlab. The results reveal that the IBCF with Cosine similarities shows the number of items recommended n top-rated movies to each user for 10 movies. The IBCF can identify the most recommended films and creates a frequency distribution of items.\",\"PeriodicalId\":334856,\"journal\":{\"name\":\"International Journal of Artificial Intelligence Research\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Artificial Intelligence Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.29099/ijair.v6i1.310\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Artificial Intelligence Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.29099/ijair.v6i1.310","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

近年来,电影越来越受欢迎。每年有成千上万部电影被制作出来。这些电影使得电影爱好者很难挑选出理想的电影来观看。我们提出了一个推荐系统,努力为选择电影提供指导。根据所采用的方法,推荐系统可以分为三组:协同过滤、基于内容的过滤和混合过滤。本文采用了推荐系统中常用的协同过滤方法。协同过滤方法有两种方式:基于用户的协同过滤(UBCF)和基于项的协同过滤(IBCF)。有两种方法可以找到相似的项目或用户:余弦和皮尔逊相似度。余弦相似度方法是确定两个项目相似度的一种方法。此外,皮尔逊相关系数方法是最广泛使用的,它通过计算两组之间的线性相关性来确定对象之间的相似性。本研究旨在确定哪个系统在IBCF中产生最高的项目相似性,并使用90%的训练数据和10%的测试数据预测评分与实际评分。从MovieLens.org获取的数据集由来自1664部电影的943名用户组成,有99392个评分。MovieLens的数据收集将使用RStudio和R包推荐人实验室进行分析。结果表明,具有余弦相似度的IBCF显示了每个用户在10部电影中向n部最受好评的电影推荐的项目数量。IBCF可以识别最受推荐的电影,并创建项目的频率分布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Item Based Collaborative Filtering Based on Highest Item Similarity
The popularity of movies has increased in recent years. There are thousands of films produced each year. These films make it challenging for movie lovers to pick the ideal film to see. We propose a recommendation system that strives to offer guidance in selecting films.  Depending on the method employed, recommendation systems can be categorized into three groups: collaborative filtering, content-based filtering, and hybrid filtering. In this work, collaborative filtering, one of the methods frequently used in recommendation systems was used. There are two ways to the Collaborative Filtering approach: User-Based Collaborative Filtering (UBCF) and Item-Based Collaborative Filtering (IBCF). There are two methods for finding similar items or users: Cosine and Pearson similarities. The Cosine similarity approach is one way to determine how similar two items are. Additionally, the Pearson Correlation Coefficient approach, which determines similarities between objects by calculating linear correlations between two sets, is the most widely employed. This study aims to determine which system produces the highest item similarity in IBCF and predicted ratings to actual ratings using 90% training and 10% testing data. The data set taken from MovieLens.org consists of 943 users from 1664 movies with 99392 ratings. The MovieLens data collection will be analyzed with the RStudio and the R package recommenderlab. The results reveal that the IBCF with Cosine similarities shows the number of items recommended n top-rated movies to each user for 10 movies. The IBCF can identify the most recommended films and creates a frequency distribution of items.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信