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数据分析与知识发现  2019, Vol. 3 Issue (2): 21-32    DOI: 10.11925/infotech.2096-3467.2018.0497
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于标签的跨领域资源个性化推荐研究*
叶佳鑫,熊回香()
华中师范大学信息管理学院 武汉 430079
Recommending Personalized Contents from Cross-Domain Resources Based on Tags
Jiaxin Ye,Huixiang Xiong()
School of Information Management, Central China Normal University, Wuhan 430079, China
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摘要 

【目的】利用标签间的关系实现跨领域资源推荐。【方法】构建跨领域资源推荐模型, 分析标签特性并选择可用于跨领域推荐的标签。以DBSCAN算法结合标签向量实现初步的基于资源类型的跨领域资源推荐, 将TF-IDF算法与个性化标签相结合改进初步结果, 实现个性化更强的二次推荐。【结果】基于资源推荐的召回率、准确率、F值分别为0.82、0.75、0.78, 基于用户标签推荐的召回率、准确率、F值分别为0.80、0.74、0.77, 基于资源与用户推荐的结果与用户兴趣具有强关联性。【局限】用于初次推荐的标签数量较少, 难以全面反映资源特征。用于二次推荐的标签需根据用户进行选择, 采集较为困难。【结论】当不同领域中的标签具有一定关联性时, 可以通过标签实现跨领域的资源推荐。

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叶佳鑫
熊回香
关键词 标签跨领域资源聚类个性化推荐    
Abstract

[Objective] This study tries to generate personalized contents from cross-domain resources based on the relationship among online tags. [Methods] First, we proposed a cross-domain resource recommendation model. Then, we identified tags appropriate for cross-domain recommendations. Third, we combined the DBSCAN algorithm with the tag vector to obtain the initial recommendation candidates. Finally, we used the TF-IDF algorithm along with the personalized tags to improve the initial list. [Results] The recall, precision, and F-measure of the resource-based recommendation method were 0.82, 0.75, and 0.78. The recall, precision, and F-measure of the user tag based recommendation method were 0.80, 0.74, and 0.77. Our results were strongly correlated to users’ interests. [Limitations] The number of tags for the initial recommendation candidates was small, which could not fully represent the resources. It is difficult to collect tags for the second round recommendation. [Conclusions] Once tags from different domains are related to each other, we can use them to recommend contents from cross-domain resources.

Key wordsTag    Cross-Domain    Resource Clustering    Personalized Recommendation
收稿日期: 2018-05-03     
基金资助:*本文系国家社会科学基金项目“基于人类行为动力学的社交网络信息交流行为研究”(项目编号: 16BTQ076)的研究成果之一
引用本文:   
叶佳鑫,熊回香. 基于标签的跨领域资源个性化推荐研究*[J]. 数据分析与知识发现, 2019, 3(2): 21-32.
Jiaxin Ye,Huixiang Xiong. Recommending Personalized Contents from Cross-Domain Resources Based on Tags. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.0497.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0497
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