RWKV4Rec: RWKV-Based Personalized Sequential Recommendation Model

Sequential recommender systems, as a core technology for personalized services, have been widely applied across various domains including e-commerce and social networks. Transformer-based sequential recommendation models face challenges such as high resource consumption and inefficient sequential processing, limiting their deployment and real-time recommendation performance. Inspired by the efficiency of the RWKV architecture in handling long sequences in natural language processing, this paper proposes RWKV4Rec (RWKV for Sequential Recommendation), the first application of RWKV to sequential recommendation tasks. Our RWKV4Rec model leverages RWKV's efficient long-sequence processing capability and linear computational complexity, reducing resource demands and eliminating additional sequential processing operations. We design an item-RWKV block module and propose a Low-Rank Time Mix based on LoRA technology, which adaptively assigns weights to historical items at each timestep to generate expressive behavior sequence features. Combined with explicit user embeddings and personalized local item matrices, it enhances sequence modeling for efficient and accurate interest prediction. Experiments on four benchmark datasets demonstrate that RWKV4Rec outperforms state-of-the-art methods, achieving 1.80-3.76% improvements in NDCG@10.