A hybrid recommender system for product sales in a banking environment

Recommender systems have been successfully applied in many domains, including in e-commerce and entertainment to boost sales. However, most existing recommender systems employ the collaborative or community-based approach which relies on the preferences or behaviour of other users, in conjunction with that of the target user. This approach may not be applicable to domains such as banking. The two major challenges to using the collaborative approach in the banking domain are: (1) absence of explicit ratings of products by customers for recommendation purpose, (2) cold-start problem which makes it difficult to recommend products to a prospective or new customer who has no preference at all. To tackle the first issue, we develop an algorithm that implicitly infers customer preferences from transaction data. To address the second issue, we propose a hybrid recommender system that combines the item-based collaborative filtering technique (which uses the customer preference data from the algorithm) and the demographic-based approach (which uses customers’ demographics). We contribute to knowledge by developing a practical and feasible approach for implementing recommender systems that drive product marketing in the banking sector. We also discuss the performance of this approach based on 393,816 customers dataset. The hybrid approach is applicable to other domains with similar challenges.