  Sequential recommendation aims to infer user preferences from historical
interaction sequences and predict the next item that users may be interested in
the future. The current mainstream design approach is to represent items as
fixed vectors, capturing the underlying relationships between items and user
preferences based on the order of interactions. However, relying on a single
fixed-item embedding may weaken the modeling capability of the system, and the
global dynamics and local saliency exhibited by user preferences need to be
distinguished. To address these issues, this paper proposes a novel diffusion
recommendation algorithm based on multi-scale cnn and residual lstm (AREAL). We
introduce diffusion models into the recommend system, representing items as
probability distributions instead of fixed vectors. This approach enables
adaptive reflection of multiple aspects of the items and generates item
distributions in a denoising manner. We use multi-scale cnn and residual lstm
methods to extract the local and global dependency features of user history
interactions, and use attention mechanism to distinguish weights as the guide
features of reverse diffusion recovery. The effectiveness of the proposed
method is validated through experiments conducted on two real-world datasets.
Specifically, AREAL obtains improvements over the best baselines by 2.63% and
4.25% in terms of HR@20 and 5.05% and 3.94% in terms of NDCG@20 on all
datasets.
