An improved content based collaborative filtering algorithm for movie recommendations

A. Pal, Prateek Parhi, M. Aggarwal
{"title":"An improved content based collaborative filtering algorithm for movie recommendations","authors":"A. Pal, Prateek Parhi, M. Aggarwal","doi":"10.1109/IC3.2017.8284357","DOIUrl":null,"url":null,"abstract":"Recommender system comprises of two prime methods which help in providing meaningful recommendations namely, Collaborative Filtering algorithm and Content-Based Filtering. In this paper, we have used a hybrid methodology which takes advantage of both Content and Collaborative filtering algorithm into account. The algorithm discussed in this article is different from the previous work in this field as it includes a novel method to find the similar content between two items. The paper incorporates an analysis that justifies this new methodology and how it can provide practical recommendations. The above approach is tested on existing user and objects data and produced improved results when compared with other two favourite methods, Pure Collaborative Filtering, and Singular Value Decomposition.","PeriodicalId":147099,"journal":{"name":"2017 Tenth International Conference on Contemporary Computing (IC3)","volume":"14 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"37","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Tenth International Conference on Contemporary Computing (IC3)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC3.2017.8284357","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 37

Abstract

Recommender system comprises of two prime methods which help in providing meaningful recommendations namely, Collaborative Filtering algorithm and Content-Based Filtering. In this paper, we have used a hybrid methodology which takes advantage of both Content and Collaborative filtering algorithm into account. The algorithm discussed in this article is different from the previous work in this field as it includes a novel method to find the similar content between two items. The paper incorporates an analysis that justifies this new methodology and how it can provide practical recommendations. The above approach is tested on existing user and objects data and produced improved results when compared with other two favourite methods, Pure Collaborative Filtering, and Singular Value Decomposition.
改进的基于内容的电影推荐协同过滤算法
推荐系统包括协同过滤算法和基于内容的过滤两种主要的方法来提供有意义的推荐。在本文中,我们使用了一种混合方法,该方法同时考虑了内容过滤算法和协同过滤算法的优势。本文所讨论的算法不同于以往在该领域的工作,因为它包含了一种新的方法来寻找两个条目之间的相似内容。本文结合了一项分析,证明了这种新方法的合理性,以及它如何能够提供实用的建议。上述方法在现有用户和对象数据上进行了测试,并与其他两种最受欢迎的方法纯协同过滤和奇异值分解相比,产生了更好的结果。
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 求助全文
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:481959085
Book学术官方微信