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The objective of cross-domain sequential recommendation is to forecast upcoming interactions by leveraging past interactions across diverse domains. Most methods aim to utilize single-domain and cross-domain information as much as possible for personalized preference extraction and effective integration. However, on one hand, most models ignore that cross-domain information is composed of multiple single-domains when generating representations. They still treat cross-domain information the same way as single-domain information, resulting in noisy representation generation. Only by imposing certain constraints on cross-domain information during representation generation can subsequent models minimize interference when considering user preferences. On the other hand, some methods neglect the joint consideration of users' long-term and short-term preferences and reduce the weight of cross-domain user preferences to minimize noise interference. To better consider the mutual promotion of cross-domain and single-domains factors, we propose a novel model (C 2 DREIF) that utilizes Gaussian graph encoders to handle information, effectively constraining the correlation of information and capturing useful contextual information more accurately. It also employs a Top-down transformer to accurately extract user intents within each domain, taking into account the user's long-term and short-term preferences. Additionally, entropy regularized is applied to enhance contrastive learning and mitigate the impact of randomness caused by negative sample composition., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Entity alignment is a crucial task in knowledge graphs, aiming to match corresponding entities from different knowledge graphs. Due to the scarcity of pre-aligned entities in real-world scenarios, research focused on unsupervised entity alignment has become more popular. However, current unsupervised entity alignment methods suffer from a lack of informative entity guidance, hindering their ability to accurately predict challenging entities with similar names and structures. To solve these problems, we present an unsupervised multi-view contrastive learning framework with an attention-based reranking strategy for entity alignment, named AR-Align. In AR-Align, two kinds of data augmentation methods are employed to provide a complementary view for neighborhood and attribute, respectively. Next, a multi-view contrastive learning method is introduced to reduce the semantic gap between different views of the augmented entities. Moreover, an attention-based reranking strategy is proposed to rerank the hard entities through calculating their weighted sum of embedding similarities on different structures. Experimental results indicate that AR-Align outperforms most both supervised and unsupervised state-of-the-art methods on three benchmark datasets., Competing Interests: Declaration of competing interest All authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)