منابع مشابه
The Netflix Prize
In October, 2006 Netflix released a dataset containing 100 million anonymous movie ratings and challenged the data mining, machine learning and computer science communities to develop systems that could beat the accuracy of its recommendation system, Cinematch. We briefly describe the challenge itself, review related work and efforts, and summarize visible progress to date. Other potential uses...
متن کاملNetflix Recommendations for Groups
In this era of overabundant information and content, people increasingly rely on recommender systems to identify those information items that best meet their needs and interests. Movie recommender systems, like the one used by Netflix, attempt to predict which films a given person will enjoy watching. While these systems help single individuals making decisions, they provide limited support for...
متن کاملCS229: The Netflix Project
This paper investigates the combination and application of a number of machine learning applications to the Netflix Challenge. The algorithm uses extra data in addition to the Netflix training set. Namely, it uses a mapping from Netflix to features gleaned from IMDB, such as the director and genre. Using k-means clustering, the algorithm first clusters the users together by the IMDB features ea...
متن کاملCollaborative Filtering for Netflix
The Netflix movie-recommendation problem was investigated and the incremental Singular Value Decomposition (SVD) algorithm was implemented to solve the problem. Experimental results are discussed.
متن کاملBennett Netflix 100 Winchester Circle
INTRODUCTION The KDD Cup is the oldest of the many data mining competitions that are now popular [1]. It is an integral part of the annual ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). In 2007, the traditional KDD Cup competition was augmented with a workshop with a focus on the concurrently active Netflix Prize competition [2]. The KDD Cup itself in 2007 con...
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ژورنال
عنوان ژورنال: British Dental Journal
سال: 2019
ISSN: 0007-0610,1476-5373
DOI: 10.1038/s41415-019-0099-8