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DOI:
计算机系统应用英文版:2014,23(5):145-151
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基于路径融合的多图层推荐算法
(福建师范大学 软件学院, 福州 350108)
Path Integration-Based Multiple Layers Recommendation
(Faculty of Software, Fujian Normal University, Fuzhou 350108, China)
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Received:September 27, 2013    Revised:November 11, 2013
中文摘要: 图推荐算法中资源分配矩阵的计算和存储占用大量开销,为了提高基于图推荐的效率,提出一种基于路径融合的多图层混合策略推荐算法(PIML). 该算法中,为了提高推荐精度并综合考虑多种因素以给出全面推荐,基于路径融合方法,利用分流策略优化资源分配,并对时间和评分因素加权,将人口统计学和物品内容信息融入到多图层,实现基于二部图推荐. 实验结果表明:该算法没有增加时间开销,提高了推荐精度,使推荐更全面更灵活,并可实时推荐.
中文关键词: 路径融合  多图层  资源分配  分流策略
Abstract:In graph-based recommendation, the calculation and storage of resource allocation matrix take up a lot of overhead, in order to improve efficiency of algorithm. Path Integration-Based Multiple Layers Recommendation(PIML) was proposed. In the algorithm, path integration-based shunt strategy was used to dynamically allocate resources, and timestamp and ratings were added to the graph. On the other hand, demographic information and item content information were added to the multiple layers structure, considering a variety of factors, it gave more comprehensive recommendation. Experimental results show that the algorithm improves the accuracy without increasing time cost; and makes recommendation more comprehensive and can give real-time recommendation.
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基金项目:教育部规划基金(11YJA860028);福建省自然科学基金(3013J01219)
引用文本:
李宏恩,肖如良,陈洪涛,赵婷,李源鑫.基于路径融合的多图层推荐算法.计算机系统应用,2014,23(5):145-151
LI Hong-En,XIAO Ru-Liang,CHEN Hong-Tao,ZHAO Ting,LI Yuan-Xin.Path Integration-Based Multiple Layers Recommendation.COMPUTER SYSTEMS APPLICATIONS,2014,23(5):145-151