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Genre based hybrid filtering for movie recommendation engine
Journal of Intelligent Information Systems ( IF 3.4 ) Pub Date : 2021-02-18 , DOI: 10.1007/s10844-021-00637-w
Arighna Roy , Simone A. Ludwig

With the dramatic rise of internet users in the last decade, there has been a massive rise in the number of daily web searches. This leads to a plethora of data available online, which is growing by the days. A recommendation engine leverages this massive amount of data by finding patterns of user behavior. Movie recommendation for users is one of the most prevalent implementations. Although it goes way back in the history of recommendation engines, collaborative filtering is still the most predominant method when it comes to the underlying technique implemented in recommendation engines. The main reasons behind that are its simplicity and flexibility. However, collaborative filtering has always suffered from the Cold-Start problem. When a new movie enters the rating platform, we do not have any user interaction for the movie. The foundation of collaborative filtering is based on the user-movie rating. In this paper, we have proposed a hybrid filtering to combat this problem using the genre labeled for a new movie. The proposed algorithm utilizes the nonlinear similarities among various movie genres and predicts the rating of a user for the new movie with the associated genres for the movie.



中文翻译:

基于类型的电影推荐引擎混合过滤

在过去十年中,随着互联网用户的急剧增加,每天的网络搜索量已大大增加。这导致了在线提供的大量数据,并且该数据正在日渐增长。推荐引擎通过查找用户行为模式来利用大量数据。为用户推荐电影是最普遍的实现方式之一。尽管它可以追溯到推荐引擎的历史,但是当涉及到在推荐引擎中实现的基础技术时,协作过滤仍然是最主要的方法。其背后的主要原因是其简单性和灵活性。但是,协作过滤始终遭受冷启动问题的困扰。当新电影进入分级平台时,我们没有与该电影的任何用户交互。协作过滤的基础是基于用户电影评级的。在本文中,我们提出了一种混合过滤来解决这一问题,它使用了为新电影标记的类型。所提出的算法利用了各种电影类型之间的非线性相似性,并利用该电影的相关类型来预测新电影的用户评级。

更新日期:2021-02-19
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