Cross-Domain Recommendation (CDR) has been proven to effectively alleviate the data sparsity problem in Recommender System (RS). Recent CDR methods often disentangle user features into domain-invariant and domain-specific features for efficient cross-domain knowledge transfer. Despite showcasing robust performance, three crucial aspects remain unexplored for existing disentangled CDR approaches: i) The significance nuances of the interaction behaviors are ignored in generating disentangled features; ii) The user features are disentangled irrelevant to the individual items to be recommended; iii) The general knowledge transfer overlooks the user's personality when interacting with diverse items. To this end, we propose a Graph Disentangled Contrastive framework for CDR (GDCCDR) with personalized transfer by meta-networks. An adaptive parameter-free filter is proposed to gauge the significance of diverse interactions, thereby facilitating more refined disentangled representations. In sight of the success of Contrastive Learning (CL) in RS, we propose two CL-based constraints for item-aware disentanglement. Proximate CL ensures the coherence of domain-invariant features between domains, while eliminatory CL strives to disentangle features within each domains using mutual information between users and items. Finally, for domain-invariant features, we adopt meta-networks to achieve personalized transfer. Experimental results on four real-world datasets demonstrate the superiority of GDCCDR over state-of-the-art methods.

Data sparsity poses a persistent challenge in Recommender Systems (RS), driving the emergence of Cross-Domain Recommendation (CDR) as a potential remedy. However, most existing CDR methods often struggle to circumvent the transfer of domain-specific information, which are perceived as noise in the target domain. Additionally, they primarily concentrate on inter-domain information transfer, disregarding the comprehensive exploration of data within intra-domains. To address these limitations, we propose SUCCDR (SeparatingUser features withCompound samples), a novel approach that tackles data sparsity by leveraging both cross-domain knowledge transfer and comprehensive intra-domain analysis. Specifically, to ensure the exclusion of noisy domain-specific features during the transfer process, user preferences are separated into domain-invariant and domain-specific features through three efficient constraints. Furthermore, the unobserved items are leveraged to generate compound samples that intelligently merge observed and unobserved potential user-item interaction, utilizing a simple yet efficient attention mechanism to enable a comprehensive and unbiased representation of user preferences. We evaluate the performance of SUCCDR on two real-world datasets, Douban and Amazon, and compare it with state-of-the-art single-domain and cross-domain recommendation methods. The experimental results demonstrate that SUCCDR outperforms existing approaches, highlighting its ability to effectively alleviate data sparsity problem.

From reducing traffic congestion to improving transportation, autonomous vehicles have immense potential in enhancing productivity and quality of life. As a safety-critical system, autonomous vehicles must undergo extensive testing before being deployed on public roads to ensure their safety and reliability. Given the complexity and high dimensionality of testing scenarios for autonomous driving, this paper proposes a test case generation method based on an improved genetic algorithm. The LGSVL simulator is used to conduct simulation tests on the Baidu Apollo system. The experimental results demonstrate that the test cases generated by this method can effectively test various safety violations of autonomous vehicles and improve the efficiency of generating effective test cases.