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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (6): 15-21    DOI: 10.11925/infotech.2096-3467.2019.1332
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Recommending Domain Knowledge Based on Parallel Collaborative Filtering Algorithm
Yang Heng(),Wang Sili,Zhu Zhongming,Liu Wei,Wang Nan
Literature and Information Center of Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences, Lanzhou 730000, China
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Abstract  

[Objective] This paper tries to identify information needed by the users, and then makes timely and accurate recommendations. [Methods] First, we generated the candidate set through content-based recommendation algorithm and item-based collaborative filtering algorithm. Then, we used parallel MapReduce technique to improve the parallel data mining performance of the proposed method. Finally, we adopted machine learning algorithms to increase the accuracy of recommended candidates and referred, personalized documents to the users. [Results] We created the recommendation list based on articles checked by the individual user. The model’s evaluation accuracy was 78.5%, and its mean squared error was 0.22. [Limitations] The user and text features need to be further investigated. The accuracy of word segmentation and model training algorithm needs to be optimized. [Conclusions] The proposed model generates personalized recommendation lists for users, and provide good support for related services.

Key wordsRecommendation System      Collaborative Filtering      MapReduce      Machine Learning Algorithm     
Received: 31 December 2019      Published: 07 July 2020
ZTFLH:  TP391  
Corresponding Authors: Yang Heng     E-mail: yangh@llas.ac.cn

Cite this article:

Yang Heng,Wang Sili,Zhu Zhongming,Liu Wei,Wang Nan. Recommending Domain Knowledge Based on Parallel Collaborative Filtering Algorithm. Data Analysis and Knowledge Discovery, 2020, 4(6): 15-21.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.1332     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I6/15

Architecture of a Knowledge Recommendation System in the Marine Domain
数据类型 数据属性
用户特征数据 用户ID、用户姓名、用户所属专题
文本特征数据 文本ID、文本标题、文本所属专题、评分
用户行为特征数据 用户ID、被访问文本ID
Classification of Data Categories
Implementation of MapReduce Inversion Collaborative Filtering
第一次MR阶段 第二次MR阶段 第三次MR阶段
Map输入 Map输入 Map输入
{userid,itemid,score} {userid,itemid,score} {itemid,itemid,score}
Map输出 Map输出 Map输出
{itemid,userid,score} {userid,itemid,score} {itemid,itemid,score}
Reduce输入 Reduce输入 Reduce输入
{itemid,userid,score} {userid,itemid,score} {itemid,itemid,score}
Reduce输出 Reduce输出 Reduce输出
{userid,itemid,score} {itemid,itemid,score} {itemid,itemid,score}
Calculation Process of MapReduce Inversion Collaborative Filtering
Data Samples for Model Training
Model Evaluation Results
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