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数据分析与知识发现  2017, Vol. 1 Issue (5): 94-101     https://doi.org/10.11925/infotech.2096-3467.2017.05.11
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于标签簇多构面信任关系的个性化推荐算法研究*
陈梅梅(), 薛康杰
东华大学旭日工商管理学院 上海 200051
Personalized Recommendation Algorithm of Multi-faceted Trust Tensor Based on Tag Clustering
Chen Meimei(), Xue Kangjie
Glorious Sun School of Business & Management, Donghua University, Shanghai 200051, China
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摘要 

目的】在基于多构面信任关系的个性化推荐中, 解决构面难以定义以及传统信任强度计算方法的局限所导致的推荐准确性低的问题。【方法】提出一种基于标签簇的多构面信任关系定义的方法, 在标签聚类得到的标签簇基础上, 引用TF-IDF思想及Pearson相似度定义簇间和簇内信任关系, 构建有利于反映不同构面信任强度的信任张量, 并融入基于张量分解模型的个性化推荐算法中。【结果】基于Last.fm数据集的仿真实验表明: 从准确率、召回率和F1值各项指标上, 本文提出的个性化推荐算法均有良好表现, 在F1值上平均提升达2.29%。【局限】仿真实验未针对其他领域的数据集进行进一步验证, 如微博、Twitter等。【结论】基于标签簇多构面信任关系的个性化推荐算法通过有效定义并全面、客观地量化用户间信任关系, 从而实现推荐准确性的提高, 有利于社交网络环境下提供更令用户满意的资源。

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陈梅梅
薛康杰
关键词 个性化推荐UGC标签张量分解多构面信任    
Abstract

[Objective] This paper aims to solve the low accuracy issue facing personalized recommendation algorithm of multi-faceted trust tensor based on tag clustering. [Methods] First, we proposed a new method to calculate multi-faceted trust based on tag clusters. Then, we introduced the TF-IDF and Pearson similarity to indicate strength of inter-cluster and intra-cluster trust. Finally, we built recommendation mechanism based on tensor decomposition to reflect the trust intensity from different facets. [Results] We examined the new algorithm with the Last.fm dataset. The precision, recall and F1 measures were better than traditional methods. Among them, the F1 measure was increased by 2.29% on average. [Limitations] Our new algorithm needs to be examined with datasets from Weibo or Twitter. [Conclusions] The proposed algorithm could effectively increase the accuracy of recommendation by defining and quantifying trust relationship among users. It improves the user experience of social network systems.

Key wordsPersonalized Recommendation    UGC Tag    Tensor Decomposition    Multi-faceted Trust
收稿日期: 2017-02-20      出版日期: 2017-06-06
ZTFLH:  F224.39 TP391 TP181  
基金资助:*本文系国家社会科学基金项目“中国特色的网络消费调查研究”(项目编号: 10BGL027)的研究成果之一
引用本文:   
陈梅梅, 薛康杰. 基于标签簇多构面信任关系的个性化推荐算法研究*[J]. 数据分析与知识发现, 2017, 1(5): 94-101.
Chen Meimei,Xue Kangjie. Personalized Recommendation Algorithm of Multi-faceted Trust Tensor Based on Tag Clustering. Data Analysis and Knowledge Discovery, 2017, 1(5): 94-101.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.05.11      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2017/V1/I5/94
  不同$\alpha $值在F1指标下的对比
  三种算法在准确率召回率曲线上的对比
  三种算法在F1指标上的对比
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