[Objective] Filter out the information the user wants from the data of the offshore platform through technical methods, and can recommend it to users in a timely and accurate manner.[Methods] Screen the candidate set through content-based recommendation algorithm and item-based collaborative filtering algorithm, and use parallel MapReduce to improve the system's parallel data mining ability; use machine learning algorithms to improve the accuracy of recommendation candidates and truly achieve precision for users Matching, personalized recommendations.[Results] A recommendation list can be effectively generated based on the article clicked by the user. The model evaluation accuracy is 78.5%, and the root mean square error is 0.22.[Limitations] The user features and text features need to be deeply mined; the word segmentation tool is used many times during the experiment, and its accuracy depends on it. The model training algorithm needs to be optimized.[Conclusions] According to the accuracy of the experimental results, the accuracy of the model basically meets the personalized recommendations for users, and can provide good support for platform construction.
杨恒, 王思丽, 祝忠明, 刘巍, 王楠. 基于并行协同过滤算法的领域知识推荐模型研究
[J]. 数据分析与知识发现, 0, (): 1-.
Yang Heng, Wang Sili, Zhu Zhongming, Liu Wei, Wang Nan. Research on Domain Knowledge Recommendation Model Based on Parallel Collaborative Filtering Algorithm
. Data Analysis and Knowledge Discovery, 0, (): 1-.