Sequential recommendation has achieved remarkable success across various application domains due to its ability to capture dynamic user preferences. Therefore, its effectiveness significantly diminishes in user cold-start scenarios, where new users have limited or no interaction history. Current solutions typically design specialized model architectures to infer cold-start user preferences from auxiliary information, such as user attributes or social networks. However, such methods overlook compatibility with advanced sequential recommender models, preventing the efficient extraction of sequential features. To address this limitation, we propose CDMRec, a Causal Diffusion Preference Model for user cold-start sequential recommendation. CDMRec generates diffusion-based preference representations for cold-start users, which can be directly utilized by existing sequential recommendation models. The framework first constructs a Preference-Dominant Sequence (PDS) by isolating interactions most indicative of user interests, mitigating noise from irrelevant behaviors. Then, leveraging causal inference, CDMRec identifies key causal variables from PDS to condition the diffusion process, enabling the generation of personalized behavioral preferences. Extensive experiments on three public datasets demonstrate that CDMRec can be seamlessly integrated into mainstream sequential recommender models, yielding substantial performance gains in cold-start settings.

Embedding-based recommender systems rely on historical interactions to model users, which poses challenges for recommending to new users, known as the user cold-start problem. Some approaches incorporate social networks to deduce preferences based on the social circles of cold-start users to solve the problem of sparse features. However, such methods have difficulty distinguishing between superficial correlations and causal relationships in social behaviors, leading to inaccuracies in predicting user preferences. To address the aforementioned issues, we propose the Causal Contrastive Learning Recommendation (C2lRec) framework. Specifically, we causally model the inference of hidden preferences from the feature and historical behavior of warm users and predict user interactions based on such preferences. The counterfactual inference is subsequently performed to intervene and extract interactions from historical behaviors of warm users that influence their preferences, designating as primary causal variables. Additionally, we utilize the primary causal variables from users within the social circle of cold-start users to substitute the missing historical interactions of cold-start users and employ a similar causal modeling approach to uncover hidden preferences as we do with warm users. Finally, we realize causal contrastive learning to enhance the distribution of cold-start users. Extensive experiments conducted on three public datasets demonstrate that the recommendation performance of C2lRec exceeds that of state-of-the-art methods.

Sequential recommendation models generate embeddings for items through the analysis of historical user-item interactions and utilize the acquired embeddings to predict user preferences. Despite being effective in revealing personalized preferences for users, these models heavily rely on user-item interactions. However, due to the lack of interaction information, new users face challenges when utilizing sequential recommendation models for predictions, which is recognized as the cold-start problem. Recent studies, while addressing this problem within specific structures, often neglect the compatibility with existing sequential recommendation models, making seamless integration into existing models unfeasible.To address this challenge, we propose CMCLRec, a Cross-Modal Contrastive Learning framework for user cold-start RECommendation. This approach aims to solve the user cold-start problem by customizing inputs for cold-start users that align with the requirements of sequential recommendation models in a cross-modal manner. Specifically, CMCLRec adopts cross-modal contrastive learning to construct a mapping from user features to user-item interactions based on warm user data. It then generates a simulated behavior sequence for each cold-start user in turn for recommendation purposes. In this way, CMCLRec is theoretically compatible with any extant sequential recommendation model. Comprehensive experiments conducted on real-world datasets substantiate that, compared with state-of-the-art baseline models, CMCLRec markedly enhances the performance of conventional sequential recommendation models, particularly for cold-start users.