Implicit feedback (e.g., user clicks) is widely used in building recommender systems (RS). However, the inherent notorious exposure bias significantly affects recommendation performance. Exposure bias refers a phenomenon that implicit feedback is influenced by user exposure and does not precisely reflect user preference. Current methods for addressing exposure bias primarily reduce confidence in unclicked data, employ exposure models, or leverage propensity scores. Regrettably, these approaches often lead to biased estimations or elevated model variance, yielding sub-optimal results. To overcome these limitations, we propose a new method ReCRec that Reasons the C auses behind the implicit feedback for debiased R ec ommendation . ReCRec identifies three scenarios behind unclicked data—i.e., unexposed, dislike, or a combination of both. A reasoning module is employed to infer the category to which each instance pertains. Consequently, the model is capable of extracting reliable positive and negative signals from unclicked data, thereby facilitating more accurate learning of user preferences. We also conduct thorough theoretical analyses to demonstrate the debiased nature and low variance of ReCRec. Extensive experiments on both semi-synthetic and real-world datasets validate its superiority over state-of-the-art methods.

Anomaly detection of time series is vital in various scenarios with explosively growing time series data. However, the non-stationary time series degrade the performance of current anomaly detection methods, where data drift causes unpredictable changes. This paper proposes a Drift-aware Anomaly Detection (DAD) method for detecting anomalies in non-stationary time series. DAD adopts a self-attention mechanism to learn an embedding, distinguishing the anomaly embeddings from the normal embeddings. Next, the KL divergence calculates the drift deviation between two data segments at adjacent periods. Then, the drift deviation module combined with the latent vector which is used to reconstruct the original vector. During the encoding stage of the time series, the latent code is modeled using different Gaussian mixture distributions and the data reconstruction error at each time tick is regarded as an anomaly metric. Furthermore, we propose a new metric to measure the degree of drift deviation for a dataset used for a fair experiment comparison. Experimental results on several public datasets and a newly collected sensor dataset demonstrate that for the non-stationary time series anomaly detection task, DAD outperforms state-of-the-art anomaly detection models up to 11.5% on the F1score.

Graph classification is a classic problem with practical applications in many real-life scenes. Existing graph neural networks, including GCN, GAT, and GIN, are proposed to extract useful features from complex graph structures. However, most existing methods’ feature extraction and aggregation inevitably mix the useful and redundant features, which will disturb the final classification performance. In this paper, to handle the above drawback, we put forward the Local Structural Separation Hypergraph Convolutional Neural Network (LoSS) based on two discoveries: most graph classification tasks only focus on a few groups of adjacent nodes, and different categories have their specific high response bits in graph embeddings. In LoSS, we first decouple the original graph into different hypergraphs and aggregate the features in each substructure, which aims to find useful features for the final classification. Next, the low-correlation feature suppression strategy is devised to suppress the irrelevant node-level and bit-level features in the forward inference process, effectively reducing the disturbance of redundant features. Experiments on five datasets show that the proposed LoSS can effectively locate and aggregate useful hypergraph features and achieve SOTA performance compared with existing methods.