Abstract:
Traditional sequence recommendation usually ignores the importance of user and item feature information, and cannot effectively model dynamic interest preferences. Therefore, this paper proposes a sequence recommendation that integrates dynamic interest and feature information. The algorithm overcomes the impact of interest transfer by modeling the target item's dynamic interest; at the same time, it integrates user and item feature information to simulate real user behavior to improve recommended performance. Firstly, for dynamic interest modeling, an auxiliary function is used to apply the next behavior to supervise the learning of the previous hidden interest state, and a gated cycle unit with attention mechanism is used to assign different weights to the degree of influence of different interest states on the target. Then, aiming at the fusion of user and item feature information, the vanilla attention mechanism is used to assign different weights to the features that affect the target item, and the multi-head attention mechanism is used for in-depth feature extraction. Finally, the user's dynamic interest representation and user item feature representation are input to the multi-layer perceptron. Simulation experiments are carried out on the datasets of Yelp and MovieLens-1M. The results show that the performance of the proposed model outperforms some baseline models.