Abstract

How can we recommend items to users utilizing multiple types of user behavior data? Multi-behavior recommender systems leverage various types of user behavior data to enhance recommendation performance for the target behavior. These systems aim to provide personalized recommendations, thereby improving user experience, engagement, and satisfaction across different applications such as e-commerce platforms, streaming services, news websites, and content platforms. While previous approaches in multi-behavior recommendation have focused on incorporating behavioral order and dependencies into embedding learning, they often overlook the nuanced importance of individual behaviors in shaping user preferences during model training. We propose MBA (Multi-Behavior sequence-Aware recommendation via graph convolution networks), an accurate framework for multi-behavior recommendations. MBA adopts a novel approach by learning embeddings that capture both the dependencies between behaviors and their relative importance in influencing user preferences. Additionally, MBA employs sophisticated sampling strategies that consider the sequential nature of behaviors during model training, ensuring that the model effectively learns from the entire behavioral sequence. Through extensive experiments on real-world datasets, we demonstrate the superior performance of MBA compared to existing methods. MBA outperforms the best competitor, achieving improvements of up to 11.2% and 11.4% in terms of HR@10 and nDCG@10, respectively. These findings underscore the effectiveness of MBA in providing accurate and personalized recommendations tailored to individual user preferences.