Predicting Product Purchase from Inferred Customer Similarity: An Autologistic Model Approach
提出一种基于客户历史购买行为相似性的产品推荐模型，结合联合空间映射和空间选择建模，在保险公司数据上验证了其预测效果优于基准模型。
Product recommendation models are key tools in customer relationship management (CRM). This study develops a product recommendation model based on the principle that customer preference similarity stemming from prior purchase behavior is a key element in predicting current product purchase. The proposed recommendation model is dependent on two complementary methodologies: joint space mapping (placing customers and products on the same psychological map) and spatial choice modeling (allowing observed choices to be correlated across customers). Using a joint space map based on past purchase behavior, a predictive model is calibrated in which the probability of product purchase depends on the customer's relative distance to other customers on the map. An empirical study demonstrates that the proposed approach provides excellent forecasts relative to benchmark models for a customer database provided by an insurance firm.