融合多因素的专家组评分协同过滤推荐算法

doi:10.3969/j.issn.1006-2475.2015.07.006

计算机与现代化

融合多因素的专家组评分协同过滤推荐算法

广东石油化工学院实验教学部计算机中心，广东茂名525000

收稿日期:2015-04-14 出版日期:2015-07-23 发布日期:2015-07-28
作者简介:赖锦辉（1977-），女，广东茂名人，广东石油化工学院实验教学部计算机中心讲师，硕士，研究方向：网络，算法，人工智能。
基金资助:
2015年茂名市科技计划项目（201524）

Collaborative Filtering Recommendation Algorithm #br# Based on Multifactor Expert Group Scoring

Computer Center， Department of Experiment Teaching， Guangdong University of Petrochemical Technology, Maoming 525000, China

Received:2015-04-14 Online:2015-07-23 Published:2015-07-28

摘要/Abstract

摘要：

针对传统协同过滤推荐算法的不足，提出一种新的推荐算法，该算法重新诠释专家与用户的关系。首先，结合全局专业指数和局部活跃指数定义专家的条件，再选取合适的比例组成专家组，然后

按照专家的判断力以及与目标用户的相异度分配评分权重，最后定义预测评分选出最佳推荐，同时，专家组成员是动态变化的，其评分也各有权重，推荐的结果更贴近目标用户。因此，本算法推荐的信

息利用率高，推荐的结果清晰明了，在公开数据集GroupLens和Netflix上的实验结果表明，本算法预测用户评分的准确率明显优于传统算法。

关键词: 全局专业指数, 局部活跃指数, 相异度, 判断力, 协同过滤推荐算法

Abstract:

In view of the limitations of traditional collaborative filtering recommendation algorithm, this paper puts forward a new collaborative filtering recommendation

algorithm. Firstly， the paper uses the global professional index and local active index to define the requirement of experts; secondly, selects appropriate proportions users to

constitute experts group, and then assigns weights for each expert according to the expert’s judgment and dissimilarities between experts and target users, finally, defines the

predictive scores. Meanwhile, members of the group are dynamic, experts have different weights, so the recommended results are closer to the target user. Because the information

utilization is high, it can get a clear result. The experimental results on open dataset named GroupLens and Netflix show that， the algorithm in prediction success rates is

superior to the traditional method．

Key words: global professional index, local active index, dissimilarities, judgment, collaborative filtering recommendation algorithm

中图分类号:

TP391

赖锦辉. 融合多因素的专家组评分协同过滤推荐算法[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2015.07.006.

LAI Jinhui. Collaborative Filtering Recommendation Algorithm #br# Based on Multifactor Expert Group Scoring[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.07.006.

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融合多因素的专家组评分协同过滤推荐算法

Collaborative Filtering Recommendation Algorithm #br# Based on Multifactor Expert Group Scoring

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