计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 232-235.doi: 10.11896/jsjkx.201200010

• 大数据&数据科学 • 上一篇    下一篇

网上购物平台多推荐融合算法研究

朱育颉1, 刘虎沉2   

  1. 1上海大学管理学院 上海200444
    2 同济大学经济与管理学院 上海200092
  • 出版日期:2021-11-10 发布日期:2021-11-12
  • 通讯作者: 刘虎沉(huchenliu@tongji.edu.cn)
  • 作者简介:zhuyujie98@shu.edu.cn
  • 基金资助:
    国家自然科学基金项目(61773250)

Research on Multi-recommendation Fusion Algorithm of Online Shopping Platform

ZHU Yu-jie1, LIU Hu-chen2   

  1. 1 School of Management,Shanghai University,Shanghai 200444,China
    2 School of Economics and Management,Tongji University,Shanghai 200092,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:ZHU Yu-jie,born in 1998,postgraduate.Her main research interests include machine learning and so on.
  • Supported by:
    National Natural Science Foundation of China(61773250).

摘要: 推荐系统能帮助用户有效解决信息过载问题,现已被广泛应用于各大网上的购物平台。对用户而言,好的推荐算法能够帮助其从海量商品中快速准确发现符合自己需求的商品;对商家而言,及时呈现给用户恰当的物品能帮助商家实现精准营销,发掘长尾商品并推荐给感兴趣的用户以提高销售额。协同过滤、基于内容推荐是目前应用成熟的推荐方法,但这些方法存在数据疏散、冷启动、可扩展性差和多媒体信息特征难以提取等问题。因此,文中提出基于融合LR-GBDT-XGBOOST的个性化推荐算法,可有效缓解上述问题。在阿里巴巴天池大数据竞赛公开数据集上进行实验,结果显示,该算法降低了推荐稀疏性,提高了推荐精度。

关键词: 电子商务, 混合推荐, 推荐系统, 协同过滤

Abstract: The recommender system can help users solve the problem of information overload effectively and has been widely applied in major online shopping platforms.For users,a good recommendation algorithm can help them find products which meet their needs from a large number of products.For merchants,timely presentation of appropriate items to users can help merchants achieve precision marketing,discover long-tail products and recommend them to users to increase sales.Collaborative filtering and content-based recommendation are currently mature recommendation methods,but these methods have problems such as data sparsity,cold start,poor scalability,and difficulty in extracting multimedia information features.Therefore,this paper proposes a personalized recommendation algorithm based on the fusion of LR-GBDT-XGBOOST,which can effectively alleviate the above problems.Experiments are carried out under the official dataset of the Alibaba Tianchi big data competition.The results show that the proposed algorithm reduces the recommended sparsity and improves the accuracy of the recommendation.

Key words: Collaborative filtering, E-commerce, Mixed recommendation, Recommender systems

中图分类号: 

  • TP18
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