工业工程 ›› 2021, Vol. 24 ›› Issue (5): 159-164.doi: 10.3969/j.issn.1007-7375.2021.05.020

• 实践与应用 • 上一篇    

基于用户画像与协同过滤的大规模定制智能推荐算法研究

王斐, 吴清烈   

  1. 东南大学 经济管理学院,江苏 南京 211189
  • 收稿日期:2020-04-27 发布日期:2021-11-02
  • 作者简介:王斐(1994—),女,山东省人,硕士研究生,主要研究方向为电子商务与个性化推荐

A Research on an Intelligent Recommendation Algorithm Based on User Portrait and Collaborative Filtering for Mass Customization

WANG Fei, WU Qinglie   

  1. School of Economics & Management, Southeast University, Nanjing 211189, China
  • Received:2020-04-27 Published:2021-11-02

摘要: 大规模定制模式的兴起与发展有效缓解了用户对差异化、个性化产品的需求与追求定制化产品成本高昂之间的矛盾。为更高效地辅助用户在大规模定制过程中做出满意的产品定制决策,对传统面向大规模定制的推荐算法进行相应改进,并结合大规模定制的特征,提出基于用户画像的定制方案推荐算法。选用基于物品的协同过滤算法作为基础推荐算法,引入大数据工具——用户画像模型对初始推荐结果进行二次过滤,以改善传统协同过滤推荐算法易忽视用户自身兴趣偏好特征的问题,提高用户定制体验与推荐精准性。给出手机产品定制案例,对产生最终推荐结果的整个过程进行模拟与分析,验证该推荐算法的有效性和可行性。

关键词: 大规模定制, 协同过滤, 用户画像, 智能推荐, 大数据

Abstract: The rise and development of mass customization models have effectively alleviated the contradiction between users' demand for differentiated and personalized products and the high cost of pursuing customized products. In order to more efficiently assist users to make satisfactory product customization decisions in the process of mass customization, the traditional mass customization-oriented recommendation algorithm is improved accordingly, and combined with the characteristics of mass customization, a recommendation scheme based on user portraits is proposed. The item-based collaborative filtering algorithm is selected as the basic recommendation algorithm, and the big data tool user profile model is introduced to filter the initial recommendation results twice, so as to improve the traditional collaborative filtering recommendation algorithm to ignore the user's own interest preference and improve the user customization experience and recommendation accuracy. The case of mobile phone product customization is given, and the whole process of generating the final recommendation result is simulated and analyzed, which verifies the effectiveness and feasibility of the recommendation algorithm.

Key words: mass customization, collaborative filtering, user portrait, intelligent recommendation, big data

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