DIMCAR: dynamic intent modeling and context-aware recommendations in sparse data environment towards next basket prediction

Abstract

In the fast-changing world of e-commerce, the success of recommender systems is crucial for boosting user engagement and increasing sales. Conventional models often struggle with evolving user preferences and data sparsity, hindering accurate predictions. Existing Graph-based regularization mechanisms and deep learning approaches address these challenges but remain sensitive to noise and computational complexity, limiting their effectiveness in large-scale, real-time settings. We propose a novel multi-layered Next Basket Recommender System called dynamic intent modelling and context-aware recommendation (DIMCAR) model to overcome these limitations. First, we resolve the data sparsity problem by constructing a novel optimized Graph Sparse Regularization framework for Non-negative Matrix Factorization (OGSR-NMF) framework integrating a time-varying graph structure, a novel hybrid sparsity norm, a modified Proximal Alternating Linearized Minimization (mPALM). Additionally, we dynamically model user intents and context using attention mechanisms and Gated Recurrent Units (GRUs). Finally, we integrate a novel Adaptive Reptile Basket Optimization Algorithm into a Deep Convolutional Neural Network, enhancing the model's adaptability to changing user behaviours in real time. Theoretical analysis and experiments on four benchmark datasets demonstrate that DIMCAR outperforms existing models in recommendation accuracy and user satisfaction.