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Factorization machines (FMs) are widely used in recommender systems due to their adaptability and ability to learn from sparse data. However, for the ubiquitous non-interactive features in sparse data, existing FMs can only estimate the parameters corresponding to these features via the inner product of their embeddings. Undeniably, they cannot learn the direct interactions of these features, which limits the model's expressive power. To this end, we first present MixFM, inspired by Mixup, to generate auxiliary training data to boost FMs. Unlike existing augmentation strategies that require labor costs and expertise to collect additional information such as position and fields, these augmented data are only by the convex combination of the raw ones without any professional knowledge support. More importantly, if non-interactive features exist in parent samples to be mixed respectively, MixFM will establish their direct interactions. Second, considering that MixFM may generate redundant or even detrimental instances, we further put forward a novel Factorization Machine powered by Saliency-guided Mixup (denoted as SMFM). Guided by the customized saliency, SMFM can generate more informative neighbor data. Through theoretical analysis, we prove that the proposed methods minimize the upper bound of the generalization error, which positively enhances FMs. Finally, extensive experiments on seven datasets confirm that our approaches are superior to baselines. Notably, the results also show that "poisoning" mixed data benefits the FM variants.

Recent studies have shown that recommender systems are vulnerable, and it is easy for attackers to inject well-designed malicious profiles into the system, resulting in biased recommendations. We cannot deprive these data's injection right and deny their existence's rationality, making it imperative to study recommendation robustness. Despite impressive emerging work, threat assessment of the bi-level poisoning problem and the imperceptibility of poisoning users remain key challenges to be solved. To this end, we propose Infmix, an efficient poisoning attack strategy. Specifically, Infmix consists of an influence-based threat estimator and a user generator, Usermix. First, the influence-based estimator can efficiently evaluate the user's harm to the recommender system without retraining, which is challenging for existing attacks. Second, Usermix, a distribution-agnostic generator, can generate unnoticeable fake data even with a few known users. Under the guidance of the threat estimator, Infmix can select the users with large attacking impacts from the quasi-real candidates generated by Usermix. Extensive experiments demonstrate Infmix's superiority by attacking six recommendation systems with four real datasets. Additionally, we propose a novel defense strategy, adversarial poisoning training (APT). It mimics the poisoning process by injecting fake users (ERM users) committed to minimizing empirical risk to build a robust system. Similar to Infmix, we also utilize the influence function to solve the bi-level optimization challenge of generating ERM users. Although the idea of "fighting fire with fire" in APT seems counterintuitive, we prove its effectiveness in improving recommendation robustness through theoretical analysis and empirical experiments.

Automatically solving math word problems (MWPs) is a challenging task for artificial intelligence (AI) and machine learning (ML) research, which aims to answer the problem with a mathematical expression. Many existing solutions simply model the MWP as a sequence of words, which is far from precise solving. To this end, we turn to how humans solve MWPs. Humans read the problem part-by-part and capture dependencies between words for a thorough understanding and infer the expression precisely in a goal-driven manner with knowledge. Moreover, humans can associate different MWPs to help solve the target with related experience. In this article, we present a focused study on an MWP solver by imitating such procedure. Specifically, we first propose a novel hierarchical math solver (HMS) to exploit semantics in one MWP. First, to imitate human reading habits, we propose a novel encoder to learn the semantics guided by dependencies between words following a hierarchical "word-clause-problem" paradigm. Next, we develop a goal-driven tree-based decoder with knowledge application to generate the expression. One step further, to imitate human associating different MWPs for related experience in problem-solving, we extend HMS to the Relation-enHanced Math Solver (RHMS) to utilize the relation between MWPs. First, to capture the structural similarity relation, we develop a meta-structure tool to measure the similarity based on the logical structure of MWPs and construct a graph to associate related MWPs. Then, based on the graph, we learn an improved solver to exploit related experience for higher accuracy and robustness. Finally, we conduct extensive experiments on two large datasets, which demonstrates the effectiveness of the two proposed methods and the superiority of RHMS.

Traffic anomalies, such as traffic accidents and unexpected crowd gathering, may endanger public safety if not handled timely. Detecting traffic anomalies in their early stage can benefit citizens' quality of life and city planning. However, traffic anomaly detection faces two main challenges. First, it is challenging to model traffic dynamics due to the complex spatiotemporal characteristics of traffic data. Second, the criteria of traffic anomalies may vary with locations and times. In this article, we propose a spatiotemporal graph convolutional adversarial network (STGAN) to address these above challenges. More specifically, we devise a spatiotemporal generator to predict the normal traffic dynamics and a spatiotemporal discriminator to determine whether an input sequence is real or not. There are high correlations between neighboring data points in the spatial and temporal dimensions. Therefore, we propose a recent module and leverage graph convolutional gated recurrent unit (GCGRU) to help the generator and discriminator learn the spatiotemporal features of traffic dynamics and traffic anomalies, respectively. After adversarial training, the generator and discriminator can be used as detectors independently, where the generator models the normal traffic dynamics patterns and the discriminator provides detection criteria varying with spatiotemporal features. We then design a novel anomaly score combining the abilities of two detectors, which considers the misleading of unpredictable traffic dynamics to the discriminator. We evaluate our method on two real-world datasets from New York City and California. The experimental results show that the proposed method detects various traffic anomalies effectively and outperforms the state-of-the-art methods. Furthermore, the devised anomaly score achieves more robust detection performances than the general score.

Quantitative understanding of relationships between students' behavioural patterns and academic performances is a significant step towards personalized education. In contrast to previous studies that were mainly based on questionnaire surveys, recent literature suggests that unobtrusive digital data bring us unprecedented opportunities to study students' lifestyles in the campus. In this paper, we collect behavioural records from undergraduate students' ( N = 18 960) smart cards and propose two high-level behavioural characters, orderliness and diligence. The former is a novel entropy-based metric that measures the regularity of campus daily life, which is estimated here based on temporal records of taking showers and having meals. Empirical analyses on such large-scale unobtrusive behavioural data demonstrate that academic performance (GPA) is significantly correlated with orderliness. Furthermore, we show that orderliness is an important feature to predict academic performance, which improves the prediction accuracy even in the presence of students' diligence. Based on these analyses, education administrators could quantitatively understand the major factors leading to excellent or poor performance, detect undesirable abnormal behaviours in time and thus implement effective interventions to better guide students' campus lives at an early stage when necessary., (© 2018 The Author(s).)