Movie Recommendation System Using Filtering Techniques

Vaibhav Ghule, Tanmay Ghormade, Sujal Lanjewar, P. Sambhare
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引用次数: 0

Abstract

In today’s world many prestigious applications used recommendation systems. Some of the information filtering algorithms are used to predict preferences as per user requirements. This system saves users time in searching and provides the best user experience. Books, news, music, articles, movies, videos, etc. are the most popular areas where recommendation systems are used. When we take reference from several research papers, one thing is clear Content-Based Filtering Algorithm is used for the recommendation system. In this paper, we work on several algorithms and manipulation of results of this we get a final recommendation list based on ratings like dislike content, related content, etc. Keywords: -Contest Based Filtering, Movie Recommendation System, Vector Similarity, Text to vector, K NN Algorithm, Cosine Similarity.
使用过滤技术的电影推荐系统
在当今世界,许多著名的应用程序都使用了推荐系统。一些信息过滤算法用于根据用户需求预测偏好。该系统节省了用户的搜索时间,提供了最佳的用户体验。书籍、新闻、音乐、文章、电影、视频等是推荐系统最常用的领域。当我们参考几篇研究论文时,有一点是明确的,即推荐系统使用了基于内容的过滤算法。在本文中,我们研究了几种算法,并对结果进行了操作,我们根据不喜欢的内容、相关内容等评级得到了最终的推荐列表。关键词:基于比赛的过滤,电影推荐系统,向量相似度,文本到向量,K神经网络算法,余弦相似度。
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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