一种自适应的混合协同过滤推荐算法

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英文篇名：An Adaptive Hybrid Collaborative Filtering Recommendation Algorithm 作者：杨佳莉 ; 李直旭 ; 许佳捷 ; 赵朋朋 ; 赵雷 ; 周晓方 英文作者：YANG Jiali;LI Zhixu;XU Jiajie;ZHAO Pengpeng;ZHAO Lei;ZHOU Xiaofang;College of Computer Science and Technology,Soochow University;Guangdong Key Laboratory of Big Data Analysis and Processing;School of Information Technology and Electrical Engineering,The University of Queensland; 关键词：推荐系统 ; 张量分解 ; 协同过滤算法 ; 自适应混合 ; 短路径 英文关键词：recommendation system;;tensor decomposition;;collaborative filtering algorithm;;adaptive hybrid;;short path 中文刊名：JSJC 英文刊名：Computer Engineering 机构：苏州大学计算机科学与技术学院;广东省大数据分析与处理重点实验室;昆士兰大学信息技术与电子工程学院; 出版日期：2019-07-15 出版单位：计算机工程 年：2019 期：v.45;No.502 基金：国家自然科学基金(61632016);; 江苏省高等学校自然科学研究重大项目(17KJA520003) 语种：中文; 页：JSJC201907036 页数：7 CN：07 ISSN：31-1289/TP 分类号：228-234

摘要

为解决协同过滤算法在处理数据量较大时存在推荐效率低的问题,提出一种自适应混合协同推荐算法。根据待推荐用户活跃度和目标物品新鲜度调节模型权重,基于张量分解计算物品间的相似度,通过短路径枚举叠加生成预测结果。实验结果表明,与CBCF算法相比,该算法推荐准确率提高了28.6%。
        In order to solve the problem that the collaborative filtering algorithm has low recommendation efficiency when processing a large amount of data,an adaptive hybrid collaborative recommendation algorithm is proposed.The algorithm adjusts the weight of the model based on the to-be-recommended user activity and the freshness of target items.The similarity between items is calculated based on the tensor decomposition.The prediction result is generated based on short path enumeration superposition.Experimental results show that compared with the CBCF algorithm,the proposed algorithm improves the recommendation accuracy by 28.6%.

引文

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