A feature-based regression algorithm for cold-start recommendation
A feature-based regression algorithm for cold-start recommendation
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DOI：
10.1080/21681015.2013.879394
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发表时间：
2014-01
影响因子：
4.5
通讯作者：
Xiujuan Xu;Lizhong Zhu;Xiaowei Zhao;Zhenzhen Xu;Liu Yu
中科院分区：
文献类型：
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作者：
Xiujuan Xu;Lizhong Zhu;Xiaowei Zhao;Zhenzhen Xu;Liu Yu
推荐系统广泛用于帮助用户选择相关的在线信息。推荐系统的一个关键挑战是为冷启动用户或冷启动项目提供高质量的推荐。我们提出了一种具有基线估计的基于特征的回归算法，以应对三种类型的冷启动问题：冷启动系统、冷启动用户和冷启动项目。我们考虑用户和项目的所有可用信息来解决冷启动问题，并考虑协同过滤系统中存在的用户和项目效应。与一些现有算法相比，我们的算法在 100k MovieLens 数据集上进行冷启动推荐是有效的。
Recommender systems are widely used to help user select relevant online information. A key challenge of recommender systems is to provide high-quality recommendations for cold-start users or cold-start items. We propose a feature-based regression algorithm with baseline estimates to cope with three types of cold-start problems: cold-start system, cold-start users, and cold-start items. We consider all available information of users and items to solve the cold-start problems and take into account the user and item effects that exist in collaborative filtering systems. Compared to some existing algorithms, our algorithm is effective on the 100 k MovieLens data-set for cold-start recommendation.