SEQUENTIAL RECOMMENDATION METHOD BASED ON LONG-TERM INTEREST AND SHORT-TERM INTEREST

摘要

Provided is a sequential recommendation method based on long-term interest and short-term interest, the method comprising: processing user purchase sequence data and user questioning data in a data set to obtain sequential interaction data of a user and a commodity, and extracting comment content provided by the user regarding the commodity to represent a feature of the commodity; then, using a recurrent neural network to learn stable long-term preferences of the user from historical purchase sequence data of the user, and using questioning data to perform modeling on instant interest of the user; and finally, for the stable long-term preferences and dynamic instant interest, using an attention mechanism to portray the degrees of dependence of different users with regard to the two features. Accordingly, the problem of inaccurate recommendation caused by user preference evolution can be effectively solved, and the different degrees of dependence of different users with regard to long-term preferences and instant interest can be effectively represented.