基于大数据技术的农产品智能推荐方法研究
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TP391.3

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国家星火计划项目(2015GA660004);吉林省重点科技研发项目(20180201073SF)


Research on Intelligent Recommendation Method of Agricultural Products Based on Big Data Technology
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    摘要:

    随着智慧农业与大数据智能的兴起,农产品电商平台智能推荐方法正成为高效满足个性化需求的重要手段。针对传统推荐方法存在的耗时长、效率低问题,本研究提出了基于大数据处理技术的农产品智能推荐方法。该方法首先将文档主题算法与矩阵分解算法混合,形成文档主题与矩阵分解混合算法;然后,将基于物品的协同过滤算法和文档主题与矩阵分解混合算法进行加权融合;最后,搭建Spark并行化计算平台,抓取京东商城和中国农产品网销售评分、评论等数据,进行特征提取、加权融合、智能推荐、误差测评。实验结果表明:文档主题与矩阵分解混合算法可有效提高推荐准确率;主题加权融合协同过滤算法可提高多样性;农产品智能推荐方法在推荐质量及执行效率方面具有明显提升。

    Abstract:

    With the rising of intelligent agriculture and big data intelligence, the intelligent recommendation method of e-commerce platform for agricultural products has becoming an important measure to satisfy the personalized needs efficiently. Aiming at the problems of long time-consuming and low efficiency of traditional recommendation methods, this paper proposes an intelligent recommendation method of agricultural products based on big data processing technology. In this method, a kind of LDA-MF hybrid algorithm was formed by integrating the document theme algorithm and matrix factorization algorithm. Second, weighting the fusion of collaborative filtering algorithm based on the item and LDA-MF hybrid algorithm. Finally, a Spark parallel computing platform is built to capture the sales scoring and commentary data of jd.com mall and China Agricultural Products Network, then feature extraction, weighted fusion, intelligent recommendation and error evaluation will be carried out. Experimental results show that the LDA-MF hybrid algorithm can effectively improve the precision of recommendation, the theme weighted fusion collaborative filtering algorithm can improve diversity, and the intelligent recommendation method of agricultural products has been obviously improved in recommending quality and execution efficiency.

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傅思维,陈桂芬,赵姗.基于大数据技术的农产品智能推荐方法研究[J].东北农业科学,2020,45(6):140-144.

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  • 收稿日期:2018-12-05
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  • 在线发布日期: 2024-11-26
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