Manipulation-resistant collaborative filtering systems
Proceedings of the third ACM conference on Recommender systems
Incorporating neighborhood effects in customer relationship management models
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Including spatial interdependence in customer acquisition models: A cross-category comparison
Expert Systems with Applications: An International Journal
| Hi-index | 0.01 |
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.