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Hybrid Recommender Systems: Content-Boosted Collaborative Filtering for Improved Recommendations

机译:混合推荐系统:内容增强型协同过滤,以改善建议

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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 explains the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, a system that combines content boosted recommendation and collaborative Filtering to recommend restaurants.
机译:推荐系统代表用户偏好,以建议要购买或检查的物品。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。本文介绍了实际的和可能的混合推荐者的概况,并介绍了一种新颖的混合者,该系统结合了内容增强的推荐和协同过滤功能来推荐餐馆。

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