Information Systems最新文献

In this paper, we discuss methods to assess the interestingness of a query in an environment of data cubes. We assume a hierarchical multidimensional database, storing data cubes and level hierarchies. We start with a comprehensive review of related work in the fields of human behavior studies and computer science. We define the interestingness of a query as a vector of scores along different aspects, like novelty, relevance, surprise and peculiarity and complement this definition with a taxonomy of the information that can be used to assess each of these aspects of interestingness. We provide both syntactic (result-independent) and extensional (result-dependent) checks, measures and algorithms for assessing the different aspects of interestingness in a quantitative fashion. We also report our findings from a user study that we conducted, analyzing the significance of each aspect, its evolution over time and the behavior of the study’s participants.

The aim of session-based recommendation (SBR) mainly analyzes the anonymous user’s historical behavior records to predict the next possible interaction item and recommend the result to the user. However, due to the anonymity of users and the sparsity of behavior records, recommendation results are often inaccurate. The existing SBR models mainly consider the order of items within a session and rarely analyze the complex transition relationship between items, and additionally, they are inadequate at mining higher-order hidden relationship between different sessions. To address these issues, we propose a topic relation heterogeneous multi-level cross-item information graph neural network (TRHMCI-GNN) to improve the performance of recommendation. The model attempts to capture hidden relationship between items through topic classification and build a topic relation heterogeneous cross-item global graph. The graph contains inter-session cross-item information as well as hidden topic relation among sessions. In addition, a self-loop star graph is established to learn the intra-session cross-item information, and the self-connection attributes are added to fuse the information of each item itself. By using channel-hybrid attention mechanism, the item information of different levels is pooled by two channels: max-pooling and mean-pooling, which effectively fuse the item information of cross-item global graph and self-loop star graph. In this way, the model captures the global information of the target item and its individual features, and the label smoothing operation is added for recommendation. Extensive experimental results demonstrate that the recommendation performance of TRHMCI-GNN model is superior to the comparable baseline models on the three real datasets Diginetica, Yoochoose1/64 and Tmall. The code is available now.¹

Fake news, hate speech and offensive language are related evil triplets currently affecting modern societies. Text modality for the computational detection of these phenomena has been widely used. In recent times, multimodal studies in this direction are attracting a lot of interests because of the potentials offered by other modalities in contributing to the detection of these menaces. However, a major problem in multimodal content understanding is how to effectively model the complementarity of the different modalities due to their diverse characteristics and features. From a multimodal point of view, the three tasks have been studied mainly using image and text modalities. Improving the effectiveness of the diverse multimodal approaches is still an open research topic. In addition to the traditional text and image modalities, we consider image–texts which are rarely used in previous studies but which contain useful information for enhancing the effectiveness of a prediction model. In order to ease multimodal content understanding and enhance prediction, we leverage recent advances in computer vision and deep learning for these tasks. First, we unify the modalities by creating a text representation of the images and image–texts, in addition to the main text. Secondly, we propose a multi-layer deep neural network with inter-modal attention mechanism to model the complementarity among these modalities. We conduct extensive experiments involving three standard datasets covering the three tasks. Experimental results show that detection of fake news, hate speech and offensive language can benefit from this approach. Furthermore, we conduct robust ablation experiments to show the effectiveness of our approach. Our model predominantly outperforms prior works across the datasets.

Measuring flow cardinality is one of the fundamental problems in data stream mining, where a data stream is modeled as a sequence of items from different flows and the cardinality of a flow is the number of distinct items in the flow. Many existing sketches based on estimator sharing have been proposed to deal with huge flows in data streams. However, these sketches suffer from inefficient memory usage due to allocating the same memory size for each estimator without considering the skewed cardinality distribution. To address this issue, we propose SuperGuardian to improve the memory efficiency of existing sketches. SuperGuardian intelligently separates flows with high-cardinality from the data stream, and keeps the information of these flows with the large estimator, while using existing sketches with small estimators to record low-cardinality flows. We carry out a mathematical analysis for the cardinality estimation error of SuperGuardian. To validate our proposal, we have implemented SuperGuardian and conducted experimental evaluations using real traffic traces. The experimental results show that existing sketches using SuperGuardian reduce error by 79 % - 96 % and increase the throughput by 0.3–2.3 times.

The Internet serves not only as a platform for communication, transactions, and cloud storage, but also as a vast knowledge store where both people and machines can create, manipulate, infer, and utilize data and knowledge. The Semantic Web was developed to facilitate this purpose, enabling machines to understand the meaning of data and knowledge for use in decision-making. The Resource Description Framework (RDF) forms the foundation of the Semantic Web, which is organized into layers known as the Semantic Web Layer Cake. However, RDF’s basic construct is a binary relationship in the format of $<subjectpredicateobject>$. Representing higher-order relationships with RDF requires reification, which can be cumbersome. Time-varying data is prevalent, but cannot be adequately represented using only binary relationships. We conducted a detailed review of the literature on extending RDF with temporal data, comparing approaches for representation, querying, storage, implementation, and evaluation. In addition, we briefly reviewed approaches for extending RDF with spatial, probability, and other dimensions in conjunction with temporal data.

The large number of scientific publications around the world is increasing at a rate of approximately 4%–5% per year. This fact has resulted in the need for tools that deal with relevant and high-quality publications. To address this necessity, search and reference management tools that include some recommendation algorithms have been developed. However, many of these solutions are proprietary tools and the full potential of recommender systems is rarely exploited. There are some solutions which provide recommendations for specific domains, by using ad-hoc resources. Furthermore, some other systems do not consider any personalization strategy to generate the recommendations. This paper presents ArZiGo, a web-based full prototype system for the search, management, and recommendation of scientific articles, which feeds on the Semantic Scholar Open Research Corpus, a corpus that is growing continually with more than 190M papers from all fields of science so far. ArZiGo combines different recommendation approaches within a hybrid system, in a configurable way, to recommend those papers that best suit the preferences of the users. A group of 30 human experts has participated in the evaluation of 500 recommendations in 10 research areas, 7 of which belong to the area of Computer Science and 3 to the area of Medicine, obtaining quite satisfactory results. Besides the appropriateness of the articles recommended, the execution time of the implemented algorithms has also been analyzed.

The point of interest (POI) recommendation algorithm in location based social network (LBSN) can assist people to find more appealing locations and satisfy their specific demands. However, it is challengeable to infer user’s preference due to the sparsity of the user’s check-in data. To address the problem and improve recommendation performance, this paper proposes an improved context-aware weighted matrix factorization algorithm for POI recommendation (ICWMF). It takes advantage of time factor, geographical information, and social relationship to obtain user’s preference for locations. Firstly, the Ebbinghaus forgetting curve is employed to model the influence of time attenuation, so as to reflect that user preferences change over time. In order to assign dynamic weights to unvisited POI and infer user preference, we build the implicit feedback term by modeling the geographical influence from user perspective and the social relationship. In addition, the Gaussian model is employed to construct proximity location relationship to represent the probability of locations being discovered by users. Then, it is taken as the regularization term to avoid overfitting. Finally, the objective function of weighted matrix factorization is reconstructed with the implicit feedback term and the regularization term we designed. ICWMF naturally learns two potential feature matrices during weighted matrix decomposition based on new designed objective function to achieve better recommendation results. The results of simulation experiments on Brightkite and Gowalla dataset indicate that ICWMF outperforms other four comparison methods in terms of precision and recall.

Model repair techniques aim at automatically updating a process model to incorporate behaviors that are observed in reality but are not compliant with the original model. Most state-of-the-art techniques focus on the fitness of the repaired models, with the goal of including single anomalous behaviors observed in a log in the form of the events. This often hampers the precision of the obtained models, which end up allowing much more behaviors than intended. In the quest of techniques avoiding this over-generalization pitfall, some notion of higher-level anomalous structure is taken into account. The type of structure considered is however typically limited to sequences of low-level events. In this work, we introduce a novel repair approach targeting more general high-level anomalous structures. To do this, we exploit instance graph representations of anomalous behaviors, that can be derived from the event log and the original process model. Our experiments show that considering high-level anomalies allows to generate repaired models that incorporate the behaviors of interest while maintaining precision and simplicity closer to the original model.