A personalized consideration set recommender system: A hierarchical Bayesian approach

We use a flexible hierarchical Bayes approach to provide a method for developing a personalized consideration set recommender system. The proposed method determines which products to recommend and in what order to present these recommendations. We demonstrate our method in the context of internet retail for home appliances. The empirical results show that the proposed method offers significant advantages in terms of both hit measures and exploring preference distribution. The recommender system that we develop can be used to provide personalized consideration set suggestions based on consumer preferences at the abstract level and to generate a potential list of customers for new product messages. Implications and suggestions for future research are also provided.