The cold-start problem in recommender systems has been facing a great challenge. Cross-domain recommendation can improve the performance of cold-start user recommendations in the target domain by using the rich information of users in the source domain. In cross-domain cold-start recommendation, users in target domain lack sufficient historical behaviors. Existing meta-learning-based methods depend on the feature distribution of training data and limit the adaptability in new tasks. To address these issues, we propose a meta-adversarial framework for cross-domain cold-start recommendation (MAFCDR) . Specifically, we employ a multi-level feature attention mechanism for independently learning the weights of long-term and short-term features to construct preferences of users in source domain. To migrate user representations, we train a meta-adversarial network that utilizes feature embeddings in the source domain as input and enhances the robustness and stability of the model. Then, the personalized bridge function transfers the user preferences in the source domain to the target domain. We build three cross-domain tasks using Amazon dataset and conduct extensive experiments, which demonstrate the effectiveness of the proposed model in cold-start user recommendation.

Context plays an important role in helping users to make decisions. There are hierarchical structure between contexts and aggregation characteristics within the context in real scenarios. Exist works mainly focus on exploring the explicit hierarchy between contexts, while ignoring the aggregation characteristics within the context. In this work, we explore both of them so as to improve accuracy of prediction in recommender systems. We propose a Random Partition Factorization Machines (RPFM) by adopting random decision trees to split the contexts hierarchically to better capture the local complex interplay. The intuition here is that local homogeneous contexts tend to generate similar ratings. During prediction, our method goes through from the root to the leaves and borrows from predictions at higher level when there is sparseness at lower level. Other than estimation accuracy of ratings, RPFM also reduces the over-fitting by building an ensemble model on multiple decision trees. We test RPFM over three different benchmark contextual datasets. Experimental results demonstrate that RPFM outperforms state-of-the-art context-aware recommendation methods.

Sequential recommendation help users find interesting items by modeling the dynamic user-item interaction sequences. Due to the data sparseness problem, cross-domain sequential recommendation (CDSR) are proposed. CDSR explore rich data from a source domain to improve performance of the target domain. However, most of the existing CDSR methods are difficult to capture the temporal context of sequences and only learn user preference based on interactions of single domain, which leads to suboptimal performance. To address these shortcomings, we propose a channel-enhanced contrastive cross-domain sequential recommendation model (C³DSR). To be specific, (1) we design a feature extractor, which extends attention to the channel dimension, to extract the user’s channel feature and capture the temporal contextual relationships between sequences. Then we calculate the weights of each channel by using three SE-Res2Blocks and multiply it with the channel feature to obtain user preference. (2) We concatenate the user’s single-domain representation, the cross-domain representation, and the user features to make CDSR. Contrastive learning is leveraged to enhance mutual information between two domains. Experimental results show that the proposed model achieves the significant improvement of performance compared with other CDSR models on Amazon and HVIDEO datasets.