A Multiinterest and Social Interest-Field Framework for Financial Security

Online payment has become an influential method of transaction. While improving convenience, this method of payment also brings great financial risks. Prevalidating transactions can be effective in reducing the number of frauds. For this reason, recommendation algorithms have been introduced to measure the credibility of transactions by predicting users' ratings of items. However, most algorithms deal with the relationships in social networks without distinction, mixing positive and negative information into the recommender system, which brings huge noise. And, they only generate a single interest representation for each user to measure the similarity between users and spread interest, ignoring the diversity of user interests. Moreover, they did not consider the propagation of different interests would be different. In this article, we propose a multiinterest and social interest-field framework (MISIF) for social recommendations in financial security, which introduces capsule networks into social recommendation and extends the traditional single-interest representation to user multiinterest embedding by dynamic routing (DR) and other methods to improve the expressiveness of user embedding. After that, we construct social interest fields to integrate social interests based on multiinterest embedding, which alleviates the noise in social networks and user data sparsity problems. Finally, we aggregate user multiinterest embedding and additional information through neural networks to obtain the final prediction scores. Experiments with three publicly available datasets show that our proposed MISIF framework outperforms the state-of-the-art social recommendation methods.

Automated summary

This paper proposes a multiinterest and social interest-field framework (MISIF) to improve social recommendations in financial security. By introducing capsule networks, the traditional single-interest representation is extended to user multiinterest embedding, enhancing the expressiveness of user embedding. By constructing social interest fields and integrating social interests based on multiinterest embedding, the framework alleviates noise in social networks and user data sparsity problems. The final prediction scores are obtained by aggregating user multiinterest embedding and additional information through neural networks.