Movie Recommendation System Using Collaborative Filtering

被引：0

作者：

Wu, Ching-Seh ^{[1
]}

Garg, Deepti ^{[1
]}

Bhandary, Unnathi ^{[1
]}

机构：

[1] San Jose State Univ, Dept Comp Sci, San Jose, CA 95192 USA

来源：

PROCEEDINGS OF 2018 IEEE 9TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS) | 2018年

关键词：

Collaborative filtering; recommender system; mahout; user-based recommender; item-based recommender;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

As the business needs are accelerating, there is an increased dependence on extracting meaningful information from humongous amount of raw data to drive business solutions. The same is true for digital recommendation systems which are becoming a norm for consumer industries such as books, music, clothing, movies, news articles, places, utilities, etc. These systems collect information from the users to improve the future suggestions. This paper aims to describe the implementation of a movie recommender system via two collaborative filtering algorithms using Apache Mahout. Furthermore, this paper will also focus on analyzing the data to gain insights into the movie dataset using Matplotlib libraries in Python.

引用

页码：11 / 15

页数：5

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