Sequential recommendation systems play a key role in modern information retrieval. However, existing intent-related work fails to adequately capture long-term dependencies in user behavior, i.e., the influence of early user behavior on current behavior, and also fails to effectively utilize item relevance. To this end, we propose a novel sequential recommendation framework to overcome the above limitations, called ICMA. Specifically, we combine temporal variability with position encoding that has extrapolation properties to encode sequences, thereby expanding the model’s view of user behavior and capturing long-term user dependencies more effectively. Additionally, we design a multi-view data augmentation method, i.e., based on random data augmentation methods (e.g., crop, mask, and reorder), and further introduce insertion and substitution operations to augment the sequence data from different views by utilizing item relevance. Within this framework, clustering is performed to learn intent distributions, and these learned intents are integrated into the sequential recommendation model via contrastive SSL, which maximizes consistency between sequence views and their corresponding intents. The training process alternates between the Expectation (E) step and the Maximization (M) step. Experiments on three real datasets show that our approach improves by 0.8% to 14.7% compared to most baselines.

Social recommendation enhances the learning of user preferences by incorporating user social information. Recently, graph neural network models have gradually become the subject of the social recommendation. However, most graph neural network-based approaches fail to fully learn the high-order collaborative semantics of user interest and social domains, and ignore the unique self-supervised signals in user social domains. To alleviate these problems, we propose a novel lightweight GCN-based social recommendation method SGSR that jointly models the high-order collaborative relations of user/item nodes in both domains. Meanwhile, in the process of message transmission of the bipartite graph and social graph, we respectively introduce a self-attention mechanism to measure the contributions of different nodes. In particular, to take full advantage of the self-supervised signals between user node messages in the social domain, we innovatively incorporate contrastive learning into this system to enable user-side node features to self-learn and update. Extensive experiments conducted on two real datasets demonstrate the effectiveness and necessity of our proposed approach.