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Hybrid Recommender Systems: Survey and Experiments

机译：混合推荐系统：调查和实验

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摘要

Recommender systems represent user preferences for the purpose of suggesting items to. purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.
机译：推荐系统代表用户偏好，以向其建议项目。购买或检查。它们已成为电子商务和信息访问中的基本应用程序，提供了有效修剪大型信息空间的建议，使用户可以直接选择最能满足其需求和偏好的商品。已经提出了用于执行推荐的多种技术，包括基于内容的，协作的，基于知识的以及其他技术。为了提高性能，有时将这些方法结合在混合推荐器中。本文调查了实际的和可能的混合推荐者的前景，并介绍了一种新颖的混合者EntreeC，该系统结合了基于知识的推荐和协作过滤以推荐餐馆。此外，我们表明，从系统的基于知识的部分获得的语义评级可增强协作过滤的有效性。

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