Collaborative-Guided Diffusion for Sequential Recommendation

Abstract

Sequential recommendation aims to predict the next item that aligns with user preferences based on their historical interaction sequence. Traditional sequential recommendation methods can be seen as understanding-based approaches, where the next relevant item is determined by analyzing user historical interactions and rating patterns. Recently, diffusion-based models have emerged as a promising paradigm, focusing on learning data distributions rather than explicitly mining sequential patterns. However, existing diffusion-based methods still face two limitations. Firstly, they often map discrete target items into continuous spaces through transformations, failing to accurately model target items in a way that reflects future user preferences. Secondly, existing conditionally guided diffusion models rely heavily on explicit conditions derived from intra-sequence patterns while neglecting inter-sequence collaborative signals, which hinders the robustness of user preference modeling. To bridge the gap between collaborative signals and diffusion models, we propose DCDRec, a Dual Collaborative Signal-Guided Diffusion Recommendation model. Specifically, for target item representation, we employ a cross-attention based encoder to obtain context-aware target item embeddings. For conditional guidance modeling, we incorporate social homophily theory and item-item affinity into the conditional generation process, introducing a dual collaborative signal-guided denoising mechanism to generate new items. Extensive experiments demonstrate the effectiveness of DCDRec and its superiority over state-of-the-art methods.