Sequential recommendation model integrating micro-behaviors and attribute enhancement

Abstract

Sequential recommendation predicts the items that the user may interact with next based on the time-series information of the user-item interactions, and learns the users’ dynamic preferences. However, most existing sequential recommendation models ignore the micro-behaviors and the importance of attribute information. Thus, we propose a new model named Sequential Recommendation Model Integrating Micro-behaviors and Attribute Enhancement (SRMA). First, we build a user-item interaction graph and a user-item-attribute interaction graph by introducing user and item attributes. In addition, we perform the temporal attention embedding propagation in the user-item interaction graph, in which the multi-head attention is used to learn the temporal neighborhood weights under micro-behaviors. Simultaneously, we perform the attribute attention embedding propagation in the user-item-attribute interaction graph, which learns the high-hop interactions among users, items and attributes under micro-behaviors, and assigns different weights to attributes through the attribute attention. Finally, the prediction is made by combining the embedding of each layer in the two graphs. Experiments on two real datasets show that the model has good performance.