Movie Recommendation Using Metadata Based Word2Vec Algorithm

Y. Yoon, Jun Woo Lee
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引用次数: 10

Abstract

Nowadays, recommending preferable item among huge number of item is essential on online market. Many content platforms, such as YouTube and Amazon, use recommendation techniques to recommend items. Therefore, various techniques have been studied to recommend desirable item for each users. In this paper, we propose a method for effectively recommending preferable movies for each users by using community user's movie rating information and movie metadata information with deep learning technology. The proposed method shows 0.165 performance improvement based on Rcall©100 as compared with the basline method.
基于元数据的Word2Vec算法电影推荐
如今,在网络市场上,从大量的商品中推荐最受欢迎的商品是必不可少的。许多内容平台,如YouTube和亚马逊,使用推荐技术来推荐项目。因此,已经研究了各种技术来为每个用户推荐理想的项目。本文提出了一种基于深度学习技术,利用社区用户的电影评分信息和电影元数据信息,为每个用户有效推荐优选电影的方法。与基线方法相比,基于Rcall©100的方法性能提高了0.165。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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