Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation

The user purchase behaviors are mainly influenced by their intentions (e.g., buying clothes for decoration, buying brushes for painting, etc.). Modeling a user's latent intention can significantly improve the performance of recommendations. Previous works model users' intentions by considering the predefined label in auxiliary information or introducing stochastic data augmentation to learn purposes in the latent space. However, the auxiliary information is sparse and not always available for recommender systems, and introducing stochastic data augmentation may introduce noise and thus change the intentions hidden in the sequence. Therefore, leveraging user intentions for sequential recommendation (SR) can be challenging because they are frequently varied and unobserved. In this paper, Intent contrastive learning with Cross Subsequences for sequential Recommendation (ICSRec) is proposed to model users' latent intentions. Specifically, ICSRec first segments a user's sequential behaviors into multiple subsequences by using a dynamic sliding operation and takes these subsequences into the encoder to generate the representations for the user's intentions. To tackle the problem of no explicit labels for purposes, ICSRec assumes different subsequences with the same target item may represent the same intention and proposes a coarse-grain intent contrastive learning to push these subsequences closer. Then, fine-grain intent contrastive learning is mentioned to capture the fine-grain intentions of subsequences in sequential behaviors. Extensive experiments conducted on four real-world datasets demonstrate the superior performance of the proposed ICSRec model compared with baseline methods.Comment: 10pages, 5figures, WSDM2024. arXiv admin note: text overlap with arXiv:2304.0776

Quaternion-Based Graph Convolution Network for Recommendation

Graph Convolution Network (GCN) has been widely applied in recommender systems for its representation learning capability on user and item embeddings. However, GCN is vulnerable to noisy and incomplete graphs, which are common in real world, due to its recursive message propagation mechanism. In the literature, some work propose to remove the feature transformation during message propagation, but making it unable to effectively capture the graph structural features. Moreover, they model users and items in the Euclidean space, which has been demonstrated to have high distortion when modeling complex graphs, further degrading the capability to capture the graph structural features and leading to sub-optimal performance. To this end, in this paper, we propose a simple yet effective Quaternion-based Graph Convolution Network (QGCN) recommendation model. In the proposed model, we utilize the hyper-complex Quaternion space to learn user and item representations and feature transformation to improve both performance and robustness. Specifically, we first embed all users and items into the Quaternion space. Then, we introduce the quaternion embedding propagation layers with quaternion feature transformation to perform message propagation. Finally, we combine the embeddings generated at each layer with the mean pooling strategy to obtain the final embeddings for recommendation. Extensive experiments on three public benchmark datasets demonstrate that our proposed QGCN model outperforms baseline methods by a large margin.Comment: 13 pages, 7 figures, 6 tables. Submitted to ICDE 202

Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation

Sequential Recommendation (SR) has received increasing attention due to its ability to capture user dynamic preferences. Recently, Contrastive Learning (CL) provides an effective approach for sequential recommendation by learning invariance from different views of an input. However, most existing data or model augmentation methods may destroy semantic sequential interaction characteristics and often rely on the hand-crafted property of their contrastive view-generation strategies. In this paper, we propose a Meta-optimized Seq2Seq Generator and Contrastive Learning (Meta-SGCL) for sequential recommendation, which applies the meta-optimized two-step training strategy to adaptive generate contrastive views. Specifically, Meta-SGCL first introduces a simple yet effective augmentation method called Sequence-to-Sequence (Seq2Seq) generator, which treats the Variational AutoEncoders (VAE) as the view generator and can constitute contrastive views while preserving the original sequence's semantics. Next, the model employs a meta-optimized two-step training strategy, which aims to adaptively generate contrastive views without relying on manually designed view-generation techniques. Finally, we evaluate our proposed method Meta-SGCL using three public real-world datasets. Compared with the state-of-the-art methods, our experimental results demonstrate the effectiveness of our model and the code is available

Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition

A motion trajectory is an intuitive representation form in time-space domain for a micromotion behavior of moving target. Trajectory analysis is an important approach to recognize abnormal behaviors of moving targets. Against the complexity of vehicle trajectories, this paper first proposed a trajectory pattern learning method based on dynamic time warping (DTW) and spectral clustering. It introduced the DTW distance to measure the distances between vehicle trajectories and determined the number of clusters automatically by a spectral clustering algorithm based on the distance matrix. Then, it clusters sample data points into different clusters. After the spatial patterns and direction patterns learned from the clusters, a recognition method for detecting vehicle abnormal behaviors based on mixed pattern matching was proposed. The experimental results show that the proposed technical scheme can recognize main types of traffic abnormal behaviors effectively and has good robustness. The real-world application verified its feasibility and the validity

Unlocking the Single‐Domain Epitaxy of Halide Perovskites

The growth of epitaxial semiconductors and oxides has long since revolutionized the electronics and optics fields, and continues to be exploited to uncover new physics stemming from quantum interactions. While the recent emergence of halide perovskites offers exciting new opportunities for a range of thin‐film electronics, the principles of epitaxy have yet to be applied to this new class of materials and the full potential of these materials is still not yet known. In this work, single‐domain inorganic halide perovskite epitaxy is demonstrated. This is enabled by reactive vapor phase deposition onto single crystal metal halide substrates with congruent ionic interactions. For the archetypical halide perovskite, cesium tin bromide, two epitaxial phases, a cubic phase and tetragonal phase, are uncovered which emerge via stoichiometry control that are both stabilized with vastly differing lattice constants and accommodated via epitaxial rotation. This epitaxial growth is exploited to demonstrate multilayer 2D quantum wells of a halide‐perovskite system. This work ultimately unlocks new routes to push halide perovskites to their full potential.Single‐domain halide perovskite heteroepitaxy is demonstrated and multiple epitaxial phases of archetypical halide perovskite are uncovered via stiochiometry control. The epitaxial growth is further exploited to demonstrate multilayer 2D quantum wells of a halide‐perovskite system and can ultimately enable their full potential in many emerging applications.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140019/1/admi201701003-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/140019/2/admi201701003_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/140019/3/admi201701003.pd

Comparing health-related quality of life of Dutch and Chinese patients with traumatic brain injury: Do cultural differences play a role?

Background: There is growing interest in health related quality of life (HRQoL) as an outcome measure in international trials. However, there might be differences in the conceptualization of HRQoL across different socio-cultural groups. The objectives of current study were: (I) to compare HRQoL, measured with the short form (SF)-36 of Dutch and Chinese traumatic brain injury (TBI) patients 1 year after injury and; (II) to assess whether differences in SF-36 profiles could be explained by cultural differences in HRQoL conceptualization. TBI patients are of particular interest because this is an important cause of diverse impairments and disabilities in functional, physical, emotional, cognitive, and social domains that may drastically reduce HRQoL. Methods: A prospective cohort study on adult TBI patients in the Netherlands (RUBICS) and a retrospective cohort study in China were used to compare HRQoL 1 year post-injury. Differences on subscales were assessed with the Mann-Whitney U-test. The internal consistency, interscale correlations, item-internal consistency and item-discriminate validity of Dutch and Chinese SF-36 profiles were examined. Confirmatory factor analysis was performed to assess whether Dutch and Chinese data fitted the SF-36 two factor-model (physical and mental construct). Results: Four hundred forty seven Dutch and 173 Chinese TBI patients were included. Dutch patients obtained significantly higher scores on role limitations due to emotional problems (p < .001) and general health (p < .001), while Chinese patients obtained significantly higher scores on physical functioning (p < .001) and bodily pain (p = .001). Scores on these subscales were not explained by cultural differences in conceptualization, since item- and scale statistics were all sufficient. However, differences among Dutch and Chinese patients were found in the conceptualization of the domains vitality, mental health and social functioning. Conclusions: One year after TBI, Dutch and Chinese patients reported a different pattern of HRQoL. Further, there might be cultural differences in the conceptualization of some of the SF-36 subscales, which has implications for outcome evaluation in multi-national trials

Refining Automatically Extracted Knowledge Bases Using Crowdsourcing

Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost