A hybrid approach towards movie recommendation system with collaborative filtering and association rule mining

IF 0.6 4区 综合性期刊 Q3 MULTIDISCIPLINARY SCIENCES Acta Scientiarum-technology Pub Date : 2022-03-11 DOI:10.4025/actascitechnol.v44i1.58925
Wisam Alnadem Mahmood, LaythKamil Almajmaie, A. Raheem, Saad Albawi
{"title":"A hybrid approach towards movie recommendation system with collaborative filtering and association rule mining","authors":"Wisam Alnadem Mahmood, LaythKamil Almajmaie, A. Raheem, Saad Albawi","doi":"10.4025/actascitechnol.v44i1.58925","DOIUrl":null,"url":null,"abstract":"There is a huge information stockpile available on the internet. But the available information still throws a stiff challenge to users while selecting the needed information. Such an issue can be solved by applying information filtering for locating the required information through a Recommender System. While using a RS, the users find it easy to curate and collect relevant information out of massive databanks. Though various types of RS are currently available, yet the RS developed by Collaborative Filtering techniques has proven to be the most suitable for many problems. Among the various Recommended Systems available, movie recommendation system is the most widely used one.  In this system, the recommendations will be made based on the similarities in the characteristics as exhibited by users / items. The movie recommendation system contains a huge list of user objects and item objects. This paper combines Collaborative Filtering Technique with association rules mining for better compatibility and assurance while delivering better recommendations. Hence, in the process, the produced recommendations can be considered as strong recommendations. The hybridization involving both collaborative filtering and association rules mining can provide strong, high-quality recommendations, even when enough data is unavailable. This article combines various recommendations for creating a movie recommendation system by using common filtering techniques and data mining techniques","PeriodicalId":7140,"journal":{"name":"Acta Scientiarum-technology","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Scientiarum-technology","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.4025/actascitechnol.v44i1.58925","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 1

Abstract

There is a huge information stockpile available on the internet. But the available information still throws a stiff challenge to users while selecting the needed information. Such an issue can be solved by applying information filtering for locating the required information through a Recommender System. While using a RS, the users find it easy to curate and collect relevant information out of massive databanks. Though various types of RS are currently available, yet the RS developed by Collaborative Filtering techniques has proven to be the most suitable for many problems. Among the various Recommended Systems available, movie recommendation system is the most widely used one.  In this system, the recommendations will be made based on the similarities in the characteristics as exhibited by users / items. The movie recommendation system contains a huge list of user objects and item objects. This paper combines Collaborative Filtering Technique with association rules mining for better compatibility and assurance while delivering better recommendations. Hence, in the process, the produced recommendations can be considered as strong recommendations. The hybridization involving both collaborative filtering and association rules mining can provide strong, high-quality recommendations, even when enough data is unavailable. This article combines various recommendations for creating a movie recommendation system by using common filtering techniques and data mining techniques
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于协同过滤和关联规则挖掘的电影推荐系统的混合方法
互联网上有大量的信息。但是,现有的信息仍然给用户选择所需信息带来了严峻的挑战。这个问题可以通过推荐系统应用信息过滤来定位所需的信息来解决。在使用RS时,用户可以很容易地从海量数据库中整理和收集相关信息。虽然目前有各种类型的RS,但通过协同过滤技术开发的RS已被证明是最适合解决许多问题的。在现有的各种推荐系统中,电影推荐系统是应用最广泛的一种。在这个系统中,将根据用户/项目所展示的特征的相似性来提出建议。电影推荐系统包含一个巨大的用户对象和项目对象列表。本文将协同过滤技术与关联规则挖掘相结合,在提供更好的推荐的同时,具有更好的兼容性和保证。因此,在这个过程中,提出的建议可以被认为是强有力的建议。包括协同过滤和关联规则挖掘的混合可以提供强大的、高质量的推荐,即使在没有足够的数据的情况下。本文通过使用常见的过滤技术和数据挖掘技术,结合了创建电影推荐系统的各种建议
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Acta Scientiarum-technology
Acta Scientiarum-technology 综合性期刊-综合性期刊
CiteScore
1.40
自引率
12.50%
发文量
60
审稿时长
6-12 weeks
期刊介绍: The journal publishes original articles in all areas of Technology, including: Engineerings, Physics, Chemistry, Mathematics, Statistics, Geosciences and Computation Sciences. To establish the public inscription of knowledge and its preservation; To publish results of research comprising ideas and new scientific suggestions; To publicize worldwide information and knowledge produced by the scientific community; To speech the process of scientific communication in Technology.
期刊最新文献
Numerical Integration of locally Peaked Bivariate Functions Synthesis and characterization of a new ruthenium (II) terpyridyl diphosphine complex Pesticide residues detected in Colossoma macropomum by the modified QuEChERS and GC-MS/MS methods Relationship between the rainfall index for Southern Brazil and the indexes of the Tropical Pacific and the Tropical Atlantic Oceans DNA Release from Polyaziridine Polyplexes Aided by Biomacromolecules: Effect of pH
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1