| 面向图书馆大数据知识服务的多情境兴趣推荐方法 |
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| 引用本文： | 刘海鸥,孙晶晶,苏妍嫄,张亚明.面向图书馆大数据知识服务的多情境兴趣推荐方法[J].现代情报,2018,38(6):62. |
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| 作者姓名： | 刘海鸥 孙晶晶 苏妍嫄 张亚明 |
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| 作者单位： | 1. 燕山大学经济管理学院, 河北 秦皇岛 066004;2. 燕山大学互联网+与产业发展研究中心, 河北 秦皇岛 066004 |
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| 基金项目： | 国家自然科学基金项目"云环境用户多兴趣图谱的移动商务关联性推荐模型及算法研究"（项目编号：71271186）；教育部人文社会科学基金"大数据异构OSNs情境特征挖掘的社会化信任推荐方法及其应用研究"（项目编号：17YJCZH109）；河北省自然科学基金"融合情境特征的大数据异构OSNs信任推荐模型及算法研究"（项目编号：G2017203319）；河北省自然科学基金项目"大数据异构在线社交网络复杂信息传播建模及算法研究"（项目编号：G2016203220）。 |
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| 摘 要： | 大数据环境下，推荐系统项目评分的稀疏性问题愈加突出，单兴趣表示方法也难以对用户多种情境兴趣进行准确描述，导致推荐结果精度大大降低。鉴于此，提出一种多情境兴趣表示方法，在此基础上构建面向图书馆大数据知识服务的多情境兴趣推荐模型，通过对用户多情境兴趣的层次划分，利用蚁群层次挖掘的优势来发现目标用户的若干最近邻类簇，然后根据类簇内相似用户对目标项目的评分对未评分项目进行预测，最后结合MapReduce化的大数据并行处理方法来进行协同过滤推荐。实验结果表明，多情境兴趣的建模方法改善了单兴趣建模存在的歧义推荐问题，有效缓解了大数据环境下项目评分的数据稀疏问题，MapReduce化的蚁群层次聚类方法也大大改善了推荐系统的运行效率。
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| 关 键 词： | 大数据知识服务 多情境兴趣 蚁群层次聚类 协同过滤推荐 |
A Multi Contextual Interest Recommender Method for Library Big Data Knowledge Service |
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| Authors: | Liu Haiou Sun Jingjing Su Yanyuan Zhang Yaming |
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| Institution: | 1. School of Economic & Management, Yanshan University, Qinhuangdao 066004, China;2. Research Center of Internet plus and Industry Development, Yanshan University, Qinhuangdao 066004, China |
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| Abstract: | Under the big data environment,the sparsity problem of recommendation system project becomes more and more serious.In addition,the traditional single interest representation method is difficult to accurately described,resulting in the reduced accuracy of recommendation result.In view of this,this paper put forward with a kind of multiple interest representation based on recommendation model for library big data knowledge service,by dividing the level of user interest more situations,using ant colony level mining advantage to some target user's nearest neighbor cluster.According to the cluster within the same user rating to forecast the goal of the project not scored,this paper finally implemented parallel processing method for collaborative filtering with the MapReduce data.The experimental results showed that the modeling method generates new multiple item clustering interest tree by hierarchical partition mechanism,enhanced the mining depth of situational interest,and the MapReduced ant colony clustering method also greatly reduced the overall computation time,significantly improved the efficiency of the recommendation system. |
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| Keywords: | library big data knowledge service multi contextual interest ACO hierarchical clustering CF recommendation |
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