经典推荐算法研究综述
Review of Classical Recommendation Algorithms
DOI: 10.12677/CSA.2019.99202, PDF,  被引量    国家自然科学基金支持
作者: 周春华*, 沈建京, 李 艳, 郭晓峰：信息工程大学，河南 郑州
关键词: 推荐系统；冷启动；数据稀疏；协同过滤；Recommender Systems； Cold-Start； Data Sparsity； Collaborative Filtering
摘要: 推荐系统作为一种有效的信息过滤工具，由于互联网的不断普及、个性化趋势和计算机用户习惯的改变，将变得更加流行。尽管现有的推荐系统也能成功地进行推荐，但它们仍然面临着冷启动、数据稀疏性和用户兴趣漂移等问题的挑战。本文概述了推荐系统的研究现状，对推荐算法进行了分类，介绍了几种经典的推荐算法，主要包括：基于内容的推荐算法、协同过滤推荐算法和混合推荐算法，并对推荐系统未来的研究趋势进行了展望。
Abstract: Recommender systems are effective tools of information filtering that are prevalent due to continuous popularization of the Internet, personalization trends, and changing habits of computer users. Although existing recommender systems are successful in producing decent recommendations, they still suffer from challenges such as cold-start, data sparsity, and user interest drift. This paper summarizes the research status of recommendation system, presents an overview of the field of recommender systems, describes the classical recommendation methods that are usually classified into the following three main categories: content-based, collaborative and hybrid recommendation algorithms, and prospects future research directions.
文章引用：周春华, 沈建京, 李艳, 郭晓峰. 经典推荐算法研究综述[J]. 计算机科学与应用, 2019, 9(9): 1803-1813. https://doi.org/10.12677/CSA.2019.99202

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