A Sequential Recommendation Model for Balancing Long- and Short-Term Benefits

Abstract

Typically, user behaviour occurs continuously, and considering this dynamic sequence correlation can lead to more accurate recommendations. Sequential recommendation systems have, therefore, become an important means of solving the problem of network information overload. However, existing attention mechanisms are still insufficient for modelling users’ dynamic and diverse preferences. This paper presents a recommendation model based on a multiheaded self-attention mechanism and multitemporal embeddings of long- and short-term interests (MSMT-LSI). MSMT-LSI balances users’ long- and short-term benefits through two multihead self-attention networks and finally forms a hybrid representation for recommendation. After finding the most suitable parameter combinations for the MSMT-LSI model through parameter sensitivity analysis and verifying the advantages of the long- and short-term fusion strategy, related experiments on five well-known datasets and their analysis shows that the performance of MSMT-LSI is better than that of the classical model on the same benchmark dataset.