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.
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