This paper investigates determinants of recommendation systems’ performance in an online experiment in a large European Internet footwear store. By combining transactional data and archival customer records, a unique database was compiled from which proxy variables were extracted to represent dimensions of consumer loyalty and shopping involvement. These variables were combined in regression analysis with technical characteristics of two types of algorithms employed for generating recommendations: the EMDE algorithm, relying on the LSH method, and the industry-standard CF-RS. Statistical analysis reveals that recommendations are more successful when visual data modality is combined with behavioural data. Better recommendation performance was found to be associated with lower levels of consumer involvement in shopping, as well as higher levels of trust and engagement with the vendor. Experience with the vendor showed a negative correlation with recommendation performance through both its main effect and by its interactions with other consumer-related variables.

本文研究了在欧洲一家大型互联网鞋店的在线实验中推荐系统性能的决定因素。通过结合交易数据和档案客户记录，编制了一个独特的数据库，从中提取代理变量来代表消费者忠诚度和购物参与度的维度。这些变量在回归分析中与用于生成推荐的两种算法的技术特征相结合：EMDE 算法，依赖于 LSH 方法，以及行业标准的 CF-RS。统计分析表明，当视觉数据模式与行为数据相结合时，推荐会更成功。发现更好的推荐性能与消费者参与购物的程度较低有关，以及与供应商的更高水平的信任和参与。供应商的经验表明，通过其主要影响以及与其他消费者相关变量的相互作用，推荐性能与推荐性能呈负相关。