Personal Information Prediction Based on Movie Rating Data

2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) Pub Date : 2016-10-01 DOI:10.1109/IIKI.2016.84

Bo Mei, Xiaolu Cheng, Xiaoshuang Xing, Bowu Zhang, Wei Cheng

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引用次数: 2

Abstract

Movies are a major form of entertainment in the US. There are a dozens of websites focusing on movie information. On most of the websites, ratings and reviews from the users play an important role. When a user gives a movie a certain score, the user not only reflects his taste toward that movie but also potentially exposes his personal information. In this paper, we investigated several movie genres. In each genre, movies were classified into different clusters by using expectationmaximization (EM) algorithm. The classification criteria were built upon audience movie rating scores and existing user information. As a result, a new or anonymous users personal information could be predicted when he rated movies on movie-related websites. Moreover, newly released movies could be easily classified into corresponding clusters to assistant user information discovery. The revealed personal information was very useful and could be utilized in different ways such as increasing the accuracy for delivering user-related ads.

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基于电影评分数据的个人信息预测

电影在美国是一种主要的娱乐形式。有几十个网站专注于电影信息。在大多数网站上，用户的评分和评论起着重要的作用。当用户给一部电影打某个分数时，用户不仅反映了他对这部电影的品味，还潜在地暴露了他的个人信息。在本文中，我们调查了几种电影类型。在每个类型中，使用期望最大化(EM)算法将电影分为不同的类。分类标准建立在观众电影评分分数和现有用户信息的基础上。因此，在电影相关网站对电影进行评价时，可以预测新用户或匿名用户的个人信息。此外，新发布的电影可以很容易地分类到相应的类中，以帮助用户发现信息。泄露的个人信息非常有用，可以以不同的方式加以利用，例如提高投放与用户相关的广告的准确性。

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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来源期刊

2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

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