A Heterogeneous Conversational Recommender System for Financial Products

Financial products recommendation distinguishes itself from ecommerce and web recommendation. Financial products have fewer available items, are more expensive, less frequently purchased and subject to user specific constraints. The study in financial products recommendation is quite limited and current industry application is still focusing on exploitingmachine learning techniques. Behavioral Finance theory states financial decisions are affected by psychological behavior biases, which are generally identified via conversation with professional advisors. Besides, in a conversation customer actively express subjective requirements and interests, which cannot be known from their static structured data. Inspired by that, we propose an innovative heterogeneous conversational recommender system (HConvoNet) which will consider not only customer’s static profile but also the implicit behavior biases and interests, thus is adaptive to customer. The proposed framework consists of two modules: profile module and conversation module. The profile module aims to capture customer’s important static needs, while the conversation module aims to extract behavior biases and dynamic interests. By integrating profile module and conversation module, HConvoNet can recommend financial products in an adaptive way. The experiments are conducted on three internal datasets from Ping An Insurance and try to predict customer’s purchase intention. We compare our model with several baselines and see that our proposed model has a significant improvement.

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