计算机应用与软件Issue(10)：284-290,7.DOI:10.3969/j.issn.1000-386x.2015.10.068
一种基于高阶奇异分解的个性化股票推荐算法
A HOSVD-BASED PERSONALISED STOCK RECOMMENDATION ALGORITHM
茅斯佳 1臧斌宇 2张谧1
作者信息
- 1. 复旦大学软件学院 上海 200433
- 2. 复旦大学软件学院并行处理研究所 上海 200433
- 折叠
摘要
Abstract
Through predicting fund managers’investments strategy on stocks,the algorithm helps the individual investors in making rational investments decisions.Unlike traditional algorithms that solely based on stocks’information,the algorithm learns from historical transactions record of fund managers as well as the factors of personal features of investors through high-order SVD (HOSVD)algorithm to provide the personalised recommendation for investors.Besides,for further improving recommendation quality,it integrates the non-personalised and personalised recommendations.Results of simulation experiment on a real-life stock transaction dataset show that compared with traditional non-personalised algorithm,the personalised algorithm used for recommendation gains a better performance in precision and yield rate.关键词
股票推荐/高阶奇异分解/线性回归Key words
Stock recommendation/High-order singular value decomposition (SVD)/Linear regression分类
信息技术与安全科学引用本文复制引用
茅斯佳,臧斌宇,张谧..一种基于高阶奇异分解的个性化股票推荐算法[J].计算机应用与软件,2015,(10):284-290,7.
