Diverse Characteristics of Users for Personalized Sequential Recommendation

Abstract

The dynamic sequence is a core feature in many modern recommendation systems. Transformer models have achieved significant success in machine translation, inspiring some researchers to introduce self-attention mechanisms into the sequential recommendation, yielding satisfactory results. However, these models share a common issue in that they lack consideration of the user's information, making it impossible to perform multi-level modeling of users accurately. Essentially, they are non-personalized models. To address the above challenges, this study proposes an approach called DCUPSRec (a personalized sequential recommendation model based on the diverse characteristics of users). This method establishes relationships between users and items in their historical interaction data and, within the sequential framework, models complex relationships between users based on their diverse characteristics and the impact of these relationships on recommended items. In addition, we use Stochastic Shared Embeddings (SSE) regularisation techniques to address potential overfitting problems caused by the introduction of users' diverse features. Extensive experiments using various users' features demonstrate that our approach transcends other sequential models when dealing with sparse and dense datasets and a variety of evaluation metrics.