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数据分析与知识发现  2017, Vol. 1 Issue (3): 38-45     https://doi.org/10.11925/infotech.2096-3467.2017.03.05
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于改进张量分解模型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 Based on Modified Tensor Decomposition Model
Chen Meimei(), Xue Kangjie
Glorious Sun School of Business & Management, Donghua University, Shanghai 200051, China
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摘要 

目的】在基于张量分解的个性化推荐中, 解决因UGC标签冗余、热门标签和资源影响用户个性化兴趣所导致的推荐准确性降低问题。【方法】提出一种改进的基于张量分解模型的个性化推荐算法, 引入标签综合共现结合谱聚类的方法, 借鉴TF-IDF中IDF的思想提出一种基于共现标签和资源的热门惩罚机制, 对基于<用户, 标签簇, 资源>三元关系的初始张量进行重新定义。【结果】基于Last.fm数据集的仿真实验结果表明, 从准确率、召回率和F1值各项指标上, 本文提出的算法均有良好表现, 综合共现谱聚类的引入使得推荐算法在F1值上平均提升5.91%, 基于IDF改进初始张量后的推荐算法在F1值上平均提升1.29%。【局限】未针对其他领域的数据集进行验证, 如微博、Delicious等。【结论】基于改进的张量分解模型的个性化推荐算法能够显著提高准确性, 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标签标签共现谱聚类张量分解    
Abstract

[Objective] This paper tries to improve the prediction accuracy of personalized recommendation algorithm based on the tensor decomposition model. [Methods] First, we proposed a new tensor model using spectral clustering technique based on combined tag co-occurrence. Second, we established a penalty scheme on popular tag and resource co-occurrence with the help of IDF in TF-IDF. Finally,we re-defined the initial tensor on the triplets of user, tag cluster, and resource. [Results] We examined the proposed model with dataset from Last.fm and found its precision, recall and F1 measure outperformed other algorithms. The F1 measures were increased by 5.91% and 1.29% thanks to the two proposed modifictions based on clustering and IDF. [Limitations] The proposed algorithm should be further evaluated with datasets from Weibo, Delicious, and other resources. [Conclusions] The new algorithm based on advanced tensor decomposition model could significantly improve the accuracy of resources recommendation to satisfy social network system users’ information needs.

Key wordsPersonalized Recommendation    UGC    Tag    Tag Co-occurrence    Spectral Clustering    Tensor Decomposition
收稿日期: 2016-11-10      出版日期: 1985-09-25
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. 基于改进张量分解模型的个性化推荐算法研究*[J]. 数据分析与知识发现, 2017, 1(3): 38-45.
Chen Meimei,Xue Kangjie. Personalized Recommendation Algorithm Based on Modified Tensor Decomposition Model. Data Analysis and Knowledge Discovery, 2017, 1(3): 38-45.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.03.05      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2017/V1/I3/38
  4种算法在准确率-召回率曲线上的对比
  4种算法在F1指标上的对比
数据集 准确性指标 CoSCluIDF CoSClu KmeansIDF TD
Tag8 Precision 22.69% 22.56% 22.38% 11.39%
Recall 43.61% 43.36% 42.94% 21.32%
F1 28.21% 28.05% 27.80% 13.99%
Tag20 Precision 23.67% 23.05% 22.54% 12.20%
Recall 45.80% 44.59% 43.59% 23.05%
F1 29.48% 28.71% 28.07% 15.03%
  推荐平均准确性指标对比
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