A recommendation scheme utilizing Collaborative Filtering

N. Dzugan, Lance Fannin, S. Makki
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引用次数: 2

Abstract

The proliferation of computers as handheld devices with Internet connectivity along with ecommerce and social networking sites allow the generation of huge amount of data. This data empowers the corporations and other organizations to produce meaningful business patterns from consumers' behavior. Also, they can build recommender systems to predict future social trends which can enhance their services and improve their products. For example, The recommendation systems used by companies such as Amazon, Google News, and Netflix utilize Collaborative Filtering techniques such as k-nearest neighbor (kNN) to discover what their users like and dislike. Using kNN, the system compares a primary user with all others and determines how similar their interests are to the primary user's. Doing so creates a neighborhood list, consisting of every user's similarity to the primary user. Using this list, it is easy to determine the primary user's most similar, or nearest neighbor. This nearest neighbor will then provide the basis for the primary user's recommendations. In this research, we present a realistic method to process large data sets collected from Internet for recommending bookmarks by using kNN in a variation of Collaborative Filtering called One-Class Collaborative Filtering (OCCF).
一种基于协同过滤的推荐方案
作为可连接互联网的手持设备的电脑的激增,以及电子商务和社交网站,使得大量数据得以产生。这些数据使公司和其他组织能够从消费者的行为中产生有意义的商业模式。此外,他们可以建立推荐系统来预测未来的社会趋势,从而提高他们的服务和改进他们的产品。例如,Amazon、Google News和Netflix等公司使用的推荐系统利用协同过滤技术(如k-nearest neighbor (kNN))来发现用户喜欢和不喜欢的内容。使用kNN,系统将主要用户与所有其他用户进行比较,并确定他们的兴趣与主要用户的兴趣相似程度。这样做会创建一个邻居列表,包含每个用户与主用户的相似度。使用这个列表,可以很容易地确定主用户最相似或最近的邻居。然后,这个最近的邻居将为主要用户的推荐提供基础。在这项研究中,我们提出了一种现实的方法,通过使用kNN来处理从互联网收集的大型数据集,以推荐书签,这是一种称为单类协同过滤(OCCF)的协同过滤变体。
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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