论文标题
推荐系统在Web技术中的使用和对冷状态问题的深入分析
The use of Recommender Systems in web technology and an in-depth analysis of Cold State problem
论文作者
论文摘要
在www（万维网）中，数据的动态开发和传播导致了互联网上可用的大量信息，但是用户无法在短时间内找到相关信息。因此，开发了一种称为建议系统的系统，以帮助用户通过浏览活动轻松找到其不适。换句话说，推荐系统是与大量信息进行交互的工具，可为用户提供优先考虑项目的个性化视图。多年来，他们在人工智能技术方面发展了，其中包括机器学习和数据挖掘。此外，推荐系统在电子商务，在线应用程序（例如Amazon.com，Netflix和booking.com）上进行了个性化。结果，这激发了许多研究人员将推荐系统的覆盖范围扩展到尚未真正解决的新挑战和问题领域，这主要是向新用户提出建议的情况，该新用户称为冷态（即冷启动）用户问题，其中新用户可能不会搜索新的用户可能不会产生很多信息。因此，本文的目的是通过一些有效的方法和挑战来解决上述寒冷问题，并确定和概述整个建议系统的当前状态
In the WWW (World Wide Web), dynamic development and spread of data has resulted a tremendous amount of information available on the Internet, yet user is unable to find relevant information in a short span of time. Consequently, a system called recommendation system developed to help users find their infromation with ease through their browsing activities. In other words, recommender systems are tools for interacting with large amount of information that provide personalized view for prioritizing items likely to be of keen for users. They have developed over the years in artificial intelligence techniques that include machine learning and data mining amongst many to mention. Furthermore, the recommendation systems have personalized on an e-commerce, on-line applications such as Amazon.com, Netflix, and Booking.com. As a result, this has inspired many researchers to extend the reach of recommendation systems into new sets of challenges and problem areas that are yet to be truly solved, primarily a problem with the case of making a recommendation to a new user that is called cold-state (i.e. cold-start) user problem where the new user might likely not yield much of information searched. Therfore, the purpose of this paper is to tackle the said cold-start problem with a few effecient methods and challenges, as well as identify and overview the current state of recommendation system as a whole
