DSRS: DELIGHT sequential recommender system

Sequential recommendation is becoming more critical in a variety of e-commerce platforms. The aim of sequential recommender systems is to model the dynamic preferences of users based on their previous actions and predict what they will do next. The collected user activity logs on real-world platforms could be quite long. This wealth of information provides options to follow users’ actual interests. Prior efforts primarily aimed at providing recommendations following recent behaviors. Meanwhile, the entire sequential data may not be used efficiently since early actions may influence users’ decisions at present. Furthermore, scanning the whole behavior sequence while doing inference for every user is unbearable due to the need for prompt reaction time in real-world applications. To this end, we propose the DELIGHT Sequential Recommender System (DSRS), which takes the above properties into account to recommend the next item the user might be interested in. DSRS divides the entire user behavior sequence into long- and short-term segments and models them through independent networks before integrating their learned representations. In particular, the first network learns user long-term, whereas the second one learns short-term preferences and then combines them for an efficient joint recommendation. Experimental findings across four datasets show that our model outperforms other state-of-the-art sequential models in apprehending long-term dependence.