Data Mining and Knowledge Discovery最新文献

Session-based recommendation (SR) aims to dynamically recommend items to a user based on a sequence of the most recent user-item interactions. Most existing studies on SR adopt advanced deep learning methods. However, the majority only consider a special behavior type (e.g., click), while those few considering multi-typed behaviors ignore to take full advantage of the relationships between products (items). In this case, the paper proposes a novel approach, called Substitutable and Complementary Relationships from Multi-behavior Data (denoted as SCRM) to better explore the relationships between products for effective recommendation. Specifically, we firstly construct substitutable and complementary graphs based on a user’s sequential behaviors in every session by jointly considering ‘click’ and ‘purchase’ behaviors. We then design a denoising network to remove false relationships, and further consider constraints on the two relationships via a particularly designed loss function. Extensive experiments on two e-commerce datasets demonstrate the superiority of our model over state-of-the-art methods, and the effectiveness of every component in SCRM.

Random walk with restart (RWR) is a widely-used measure of node similarity in graphs, and it has proved useful for ranking, community detection, link prediction, anomaly detection, etc. Since RWR is typically required to be computed separately for a larger number of query nodes or even for all nodes, fast computation of it is indispensable. However, for hypergraphs, the fast computation of RWR has been unexplored, despite its great potential. In this paper, we propose ARCHER, a fast computation framework for RWR on hypergraphs. Specifically, we first formally define RWR on hypergraphs, and then we propose two computation methods that compose ARCHER. Since the two methods are complementary (i.e., offering relative advantages on different hypergraphs), we also develop a method for automatic selection between them, which takes a very short time compared to the total running time. Through our extensive experiments on 18 real-world hypergraphs, we demonstrate (a) the speed and space efficiency of ARCHER, (b) the complementary nature of the two computation methods composing ARCHER, (c) the accuracy of its automatic selection method, and (d) its successful application to anomaly detection on hypergraphs.

Distilling actionable patterns from large-scale streaming data in the presence of concept drift is a challenging problem, especially when data is polluted with noisy labels. To date, various data stream mining algorithms have been proposed and extensively used in many real-world applications. Considering the functional complementation of classical online learning algorithms and with the goal of combining their advantages, we propose an Online Ensemble Classification (OEC) algorithm to integrate the predictions obtained by different base online classification algorithms. The proposed OEC method works by learning weights of different base classifiers dynamically through the classical Normalized Exponentiated Gradient (NEG) algorithm framework. As a result, the proposed OEC inherits the adaptability and flexibility of concept drift-tracking online classifiers, while maintaining the robustness of noise-resistant online classifiers. Theoretically, we show OEC algorithm is a low regret algorithm which makes it a good candidate to learn from noisy streaming data. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed OEC method.

Handling incomplete multivariate time series is an important and fundamental concern for a variety of domains. Existing time-series imputation approaches rely on basic assumptions regarding relationship information between sensors, posing significant challenges since inter-sensor interactions in the real world are often complex and unknown beforehand. Specifically, there is a lack of in-depth investigation into (1) the coexistence of relationships between sensors and (2) the incorporation of reciprocal impact between sensor properties and inter-sensor relationships for the time-series imputation problem. To fill this gap, we present the Structure-aware Decoupled imputation network (SaD), which is designed to model sensor characteristics and relationships between sensors in distinct latent spaces. Our approach is equipped with a two-step knowledge integration scheme that incorporates the influence between the sensor attribute information as well as sensor relationship information. The experimental results indicate that when compared to state-of-the-art models for time-series imputation tasks, our proposed method can reduce error by around 15%.

Benign parotid tumors follow an indolent course and present as slow-growing painless swelling in the pre-and-infra-auricular areas. The treatment of choice is surgery. Though the gold standard technique is Superficial Parotidectomy, Extracapsular Dissection (ECD) is an alternative option with the same outcome and decreased complications. This study discusses our experience with extracapsular dissection and the surgical nuances for better results. A retrospective study of histologically confirmed cases of pleomorphic adenoma of the parotid gland, who underwent Extracapsular dissection between September 2019 and March 2023, was done. The demographic details, clinical characteristics, and outcomes were evaluated. There were 33 patients, including 16 females and 17 males, with a mean age of 32.75 years. All cases presented as slow-growing painless swelling for a mean duration of 5 years. Most of the tumors (94%) were of size between 2 and 4 cm, with few tumors more than 4 cm. All underwent extracapsular dissection with complete excision. There was only one complication (seroma) and no incidence of facial palsy in our experience with ECD. The goal of a benign parotid surgery is the complete removal of the tumor with minimum complications, which could be achieved with ECD, which has good tumor clearance and lesser rates of complications with good cosmesis. Thus, this minimally invasive parotid surgery could be a worthwhile option in properly selected cases.

The field of time series anomaly detection is constantly advancing, with several methods available, making it a challenge to determine the most appropriate method for a specific domain. The evaluation of these methods is facilitated by the use of metrics, which vary widely in their properties. Despite the existence of new evaluation metrics, there is limited agreement on which metrics are best suited for specific scenarios and domains, and the most commonly used metrics have faced criticism in the literature. This paper provides a comprehensive overview of the metrics used for the evaluation of time series anomaly detection methods, and also defines a taxonomy of these based on how they are calculated. By defining a set of properties for evaluation metrics and a set of specific case studies and experiments, twenty metrics are analyzed and discussed in detail, highlighting the unique suitability of each for specific tasks. Through extensive experimentation and analysis, this paper argues that the choice of evaluation metric must be made with care, taking into account the specific requirements of the task at hand.