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A collaborative filtering model incorporating media promotions and users' variety-seeking tendencies in the digital music market
Decision Support Systems ( IF 7.5 ) Pub Date : 2023-05-30 , DOI: 10.1016/j.dss.2023.114022
Myounggu Lee, Hye-Jin Kim

Understanding customer preferences and providing the right products at the right time to customers via personalized recommendations have been among the major interests of online retailers and service providers. This paper proposes an improved collaborative filtering model that incorporates a firm's marketing effort variables (i.e., media promotional variables) to improve the prediction of customers' digital music choices. In addition, we assert that the predictive model's effectiveness is different for consumers depending on their variety-seeking tendencies in music. We compared our predictive model to benchmark models and demonstrated that our proposed model is superior in predicting users' download behavior. We also found that the overall predictive performance is higher for active variety seekers who consume diverse types of music via streaming. We provide some evidence that this may be due to differences in the degree to which the two groups are influenced by different types of media promotions. The results suggest that considering psychological characteristics such as variety-seeking tendencies provides more advantages in prediction and recommendation systems, which opens new avenues for improvement.



中文翻译:

结合数字音乐市场中媒体促销和用户多样化寻求倾向的协同过滤模型

了解客户偏好并通过个性化推荐在正确的时间向客户提供正确的产品一直是在线零售商和服务提供商的主要兴趣之一。本文提出了一种改进的协同过滤模型,该模型结合了公司的营销努力变量(即媒体促销变量)来改进对客户数字音乐选择的预测。此外,我们断言,预测模型的有效性对于消费者来说是不同的,具体取决于他们寻求音乐多样性的倾向。我们将我们的预测模型与基准模型进行了比较,并证明我们提出的模型在预测用户的下载行为方面具有优越性。我们还发现,总体对于通过流媒体消费不同类型音乐的活跃多样性寻求者来说,预测性能更高。我们提供的一些证据表明,这可能是由于这两个群体受不同类型媒体宣传影响的程度存在差异。结果表明,考虑多种心理特征(例如寻求多样性的倾向)可以在预测和推荐系统中提供更多优势,从而开辟新的改进途径。

更新日期:2023-05-30
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