Financial product recommendation system based on transformer

With the growth of the variety and quantity of Internet financial products, how to effectively implement personalized recommendation has become the key issue. The particularity of Internet financial products lies in that attribute value (such as 7-day annualized income) is not fixed. Users will consider the attribute value of the product at that time when choosing the product at different times. The importance of timing factor in financial product recommendation cannot be ignored. However, the traditional financial product recommendation algorithm is based on the static attributes of users and financial products, ignoring the factors of time in series data, and the recommendation quality is low. With the development of artificial intelligence, deep learning technology has been widely used in personalized recommendation system. Therefore, taking advantage of the advantages of transformer in processing time series, an R-Transformer(Recommendation system based on transformer) network has proposed. Two R-Transformer networks are used to mine users' and financial products' states based on time series, and the inner product of users' and financial products' states is taken as the final score. Experimental results show that compared with the traditional collaborative filtering and RNN algorithm, this algorithm can effectively reduce RMSE and improve the F-measure of prediction.