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Improving the Quality of the Personalized Electronic Program Guide

机译：提高个性化电子节目指南的质量

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As Digital TV subscribers are offered more and more channels, it is becoming increasingly difficult for them to locate the right programme information at the right time. The personalized Electronic Programme Guide (pEPG) is one solution to this problem; it leverages artificial intelligence and user profiling techniques to learn about the viewing preferences of individual users in order to compile personalized viewing guides that fit their individual preferences. Very often the limited availability of profiling information is a key limiting factor in such personalized recommender systems. For example, it is well known that collaborative filtering approaches suffer significantly from the sparsity problem, which exists because the expected item-overlap between profiles is usually very low. In this article we address the sparsity problem in the Digital TV domain. We propose the use of data mining techniques as a way of supplementing meagre ratings-based profile knowledge with additional item-similarity knowledge that can be automatically discovered by mining user profiles. We argue that this new similarity knowledge can significantly enhance the performance of a recommender system in even the sparsest of profile spaces. Moreover, we provide an extensive evaluation of our approach using two large-scale, state-of-the-art online systems―PTVPlus, a personalized TV listings portal and Fischlar, an online digital video library system.
机译：随着向数字电视用户提供越来越多的频道，对他们来说，在正确的时间定位正确的节目信息变得越来越困难。个性化的电子节目指南（pEPG）是解决此问题的一种方法。它利用人工智能和用户配置文件技术来了解各个用户的观看偏好，以便编制适合其个人偏好的个性化观看指南。在这种个性化推荐器系统中，概要信息的有限可用性经常是关键的限制因素。例如，众所周知，协同过滤方法会遭受稀疏性问题的困扰，这是因为配置文件之间的预期项目重叠通常非常低，因此存在稀疏性问题。在本文中，我们解决了数字电视领域中的稀疏性问题。我们建议使用数据挖掘技术，以补充可通过挖掘用户配置文件自动发现的其他项目相似性知识来补充基于微薄等级的配置文件知识。我们认为，即使是最稀缺的配置文件空间，这种新的相似性知识也可以显着提高推荐系统的性能。此外，我们使用两个大型的最先进的在线系统-一个个性化的电视列表门户网站PTVPlus和一个在线数字视频图书馆系统Fischlar-对我们的方法进行了广泛的评估。

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