推荐系统研究综述

近年来，推荐系统得到前所未有的关注和发展。作为电子商务的核心技术，推荐系统在帮助消费者便捷的找到所需的潜在商品同时也促进商品的销售，对于消费者和商品生产者来说都至关重要。推荐系统可以利用用户的行为信息、社交网络信息、标签数据等来提升推荐系统的质量。随着推荐系统的快速发展，如何评价推荐系统以及如何提高推荐系统的可解释性也成为热门的研究课题。从推荐算法、评测指标和可解释性三个部分对推荐系统的研究现状进行分析总结。

A Survey of Recommender System

In recent years, recommender system gets unprecedented attention and development. As the core of e-commerce technology, the recom-mender system helps consumers easily find potential commodity and also promotes the sale of goods for consumers and producers. Recom-mender system can utilize user behavior information, social network information, tag data, etc. to enhance the quality of the recommender system. With the rapid development of the recommender system, how to evaluate the system and how to improve the interpretability rec-ommendation system has also become a hot research topic. Makes detailed introduction and analysis of recommendation algorithm, evalua-tion indicators and interpretability of recommender system.