北京邮电大学学报(社科版) ›› 2018, Vol. 20 ›› Issue (5): 24-31.doi: 10.19722/j.cnki.1008-7729.2018.0132

• 电子商务 • 上一篇    下一篇

基于用户感知的个性化推荐系统效果研究——以淘宝电商平台为例

  

  1. 北京邮电大学 经济管理学院, 北京100876
  • 收稿日期:2018-06-01 出版日期:2018-10-31

Effect of Personalized Recommendation System Based on User Perception#br# ——Taking Taobao Platform as an Example

  1. School of Economics and Management, Beijing University of Posts and Telecommunication,
    Beijing 100876, China
  • Received:2018-06-01 Online:2018-10-31

摘要: 在当今信息爆炸和互联网应用丛生的时代,个性化推荐无处不在,根据用户偏好主动推荐其可能感兴趣的产品或信息,有助于将用户从繁杂的商品或信息中解放出来,并提高电子商务的商品销量。目前,针对推荐系统效果的研究较少关注用户感受。大多数研究集中于信息科学和计算机领域,从技术改良视角来研究推荐算法的准确性;一些营销、统计等领域的学者更为关注个性化推荐系统对用户行为、用户满意度的影响,一般采用理论分析、问卷调查等方法开展研究。由于用户体验和感受较为抽象,问卷调查方式的封闭性问题导致难以获得用户的真实感受。本文以淘宝电商平台为实例,采用情境访谈的方式获取用户对推荐系统的切身感受。获取用户角度更为关注的个体化推荐系统效果的指标有:感知有用性、感知安全、感知舒适性和感知易用性。最后,提出电子商务平台推荐系统的优化建议。

关键词: 个性化推荐, 用户感知, 电子商务

Abstract: In this era of information explosion, personalized recommendations have come into being. Based on user preferences, personalized recommendation systems can actively recommend products or information that they may be interested in. This can not only free users from complicated products in e-commerce, but also increase sales. Most of the existing researches are concentrated more on the field of information science and computer science to improve the accuracy of the algorithm. Some scholars in marketing, statistics and other fields pay more attention to the effect of personalized recommendation system on user behavior and user satisfaction. Most of them use methods such as theoretical analysis and questionnaires. As the user experience and feelings are more abstract, the closed questions of the questionnaire survey method are difficult to obtain the true feelings of users. Taobao platform is selected to conduct empirical researches and situational interview methods are used to obtain user feelings about the recommendation system. Meanwhile, the indicators that users pay more attention to are obtained such as perceived usefulness, perceived security, perceived comfort, and ease of use. Finally, some suggestions for the recommendation system of e-commerce platforms are put forward.

Key words: personalized recommendation, user perception, e-commerce

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