Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure

Time-aware metric embedding with asymmetric projection for successive POI recommendation

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Successive Point-of-Interest (POI) recommendation aims to recommend next POIs for a given user based on this user’s current location. Indeed, with the rapid growth of Location-based Social Networks (LBSNs), successive POI recommendation has become an important and challenging task, since it can help to meet users’ dynamic interests based on their recent check-in behaviors. While some efforts have been made for this task, most of them do not capture the following properties: 1) The transition between consecutive POIs in user check-in sequences presents asymmetric property, however existing approaches usually assume the forward and backward transition probabilities between a POI pair are symmetric. 2) Users usually prefer different successive POIs at different time, but most existing studies do not consider this dynamic factor. To this end, in this paper, we propose a time-aware metric embedding approach with asymmetric projection (referred to as MEAP-T) for successive POI recommendation, which takes the above two properties into consideration. In addition, we exploit three latent Euclidean spaces to project the POI-POI, POI-user, and POI-time relationships. Finally, the experimental results on two real-world datasets show MEAP-T outperforms the state-of-the-art methods in terms of both precision and recall

Modeling the Dynamics of Consumer Behavior from Massive Interaction Data

Recent technological innovations (e.g. e-commerce platforms, automated retail stores) have enabled dramatic changes in people's shopping experiences, as well as the accessibility to incredible volumes of consumer-product interaction data. As a result, machine learning (ML) systems can be widely developed to help people navigate relevant information and make decisions. Traditional ML systems have achieved great success on various well-defined problems such as speech recognition and facial recognition. Unlike these tasks where datasets and objectives are clearly benchmarked, modeling consumer behavior can be rather complicated; for example, consumer activities can be affected by real-time shopping contexts, collected interaction data can be noisy and biased, interests from multiple parties (both consumers and producers) can be involved in the predictive objectives.The primary goal of this dissertation is to address the obstacles in modeling consumer activities through computational approaches, but with careful considerations from economic and societal perspectives. Intellectually, such models help us to understand the forces that guide consumer behavior. Methodologically, I build algorithms capable of processing massive interaction datasets by connecting well-developed ML techniques and well-established economic theories. Practically, my work has applications ranging from recommender systems, e-commerce and business intelligence

A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model

High-dimensional and incomplete (HDI) data holds tremendous interactive information in various industrial applications. A latent factor (LF) model is remarkably effective in extracting valuable information from HDI data with stochastic gradient decent (SGD) algorithm. However, an SGD-based LFA model suffers from slow convergence since it only considers the current learning error. To address this critical issue, this paper proposes a Nonlinear PID-enhanced Adaptive Latent Factor (NPALF) model with two-fold ideas: 1) rebuilding the learning error via considering the past learning errors following the principle of a nonlinear PID controller; b) implementing all parameters adaptation effectively following the principle of a particle swarm optimization (PSO) algorithm. Experience results on four representative HDI datasets indicate that compared with five state-of-the-art LFA models, the NPALF model achieves better convergence rate and prediction accuracy for missing data of an HDI data

A Survey of the methods on fingerprint orientation field estimation

Fingerprint orientation field (FOF) estimation plays a key role in enhancing the performance of the automated fingerprint identification system (AFIS): Accurate estimation of FOF can evidently improve the performance of AFIS. However, despite the enormous attention on the FOF estimation research in the past decades, the accurate estimation of FOFs, especially for poor-quality fingerprints, still remains a challenging task. In this paper, we devote to review and categorization of the large number of FOF estimation methods proposed in the specialized literature, with particular attention to the most recent work in this area. Broadly speaking, the existing FOF estimation methods can be grouped into three categories: gradient-based methods, mathematical models-based methods, and learning-based methods. Identifying and explaining the advantages and limitations of these FOF estimation methods is of fundamental importance for fingerprint identification, because only a full understanding of the nature of these methods can shed light on the most essential issues for FOF estimation. In this paper, we make a comprehensive discussion and analysis of these methods concerning their advantages and limitations. We have also conducted experiments using publically available competition dataset to effectively compare the performance of the most relevant algorithms and methods

Recommending on graphs: a comprehensive review from a data perspective

Recent advances in graph-based learning approaches have demonstrated their effectiveness in modelling users' preferences and items' characteristics for Recommender Systems (RSS). Most of the data in RSS can be organized into graphs where various objects (e.g., users, items, and attributes) are explicitly or implicitly connected and influence each other via various relations. Such a graph-based organization brings benefits to exploiting potential properties in graph learning (e.g., random walk and network embedding) techniques to enrich the representations of the user and item nodes, which is an essential factor for successful recommendations. In this paper, we provide a comprehensive survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we start from a data-driven perspective to systematically categorize various graphs in GLRSs and analyze their characteristics. Then, we discuss the state-of-the-art frameworks with a focus on the graph learning module and how they address practical recommendation challenges such as scalability, fairness, diversity, explainability and so on. Finally, we share some potential research directions in this rapidly growing area.Comment: Accepted by UMUA

Effective graph representation learning for ranking-based recommendation

Ranking-based recommender systems are designed to generate a personalised ranking list of items for a given user to address the information overload problem. An effective and efficient ranking-based recommender system can benefit users by providing them with items of interest as well as service providers by increasing their exposure and profits. Since more and more users and providers of items have been increasingly interacting with online platforms, the underlying recommendation algorithms are facing more challenges. For example, traditional collaborative filtering-based recommender systems cannot generate effective recommendations to cold-start users due to the lack of sufficient interactions. In addition, although recommender systems can leverage deep learning-based techniques to enhance their effectiveness, they are not robust enough against variances in the models’ initialisations, which can degrade the users’ satisfaction. Furthermore, when incorporating these complex deep models, the training phases of recommender systems become less efficient, which might slower the online platforms from quickly capturing the users’ interests. Graph representation learning includes techniques that can leverage graph-structured data and generate latent representations for the nodes, graphs/sub-graphs and edges between nodes. Since the user-item interaction matrix is in fact a bipartite graph, we can use these graph-based techniques to leverage the interaction matrix and generate more effective node representations for the users and items. Therefore, this thesis aims to enhance the ranking-based recommendations by proposing novel recommender systems based on graph representation learning. In particular, this thesis uses heterogeneous graph representation learning, graph pre-training and graph contrastive learning to improve the effectiveness of ranking-based recommendations while alleviating the aforementioned cold-start problem as well as the low-robustness and low training-efficiency issues. To enhance the effectiveness of ranking-based recommendations and alleviate the cold-start problem, we propose to use the heterogeneous graph representation learning technique to encode the typical side information of the users and items, which are usually defined as the attributes of users and the descriptions of items. For example, a user-item interaction matrix, social relations are one of the most naturally available relations that can be used to enrich such an interaction matrix. Therefore, we choose the social relations among different types of side information to build the heterogeneous graph. We propose a novel recommender system, the Social-aware Gaussian Pre-trained model (SGP), which encodes the user social relations and interaction data using the heterogeneous graph representation learning technique. Next, in the subsequent fine-tuning stage, our SGP model adopts a Gaussian Mixture Model (GMM) to factorise these pre-trained embeddings for further training. Our extensive experiments on three public datasets show that SGP can alleviate the cold-start problem while also ensuring effective recommendations for regular users. To alleviate the low-robustness issue and enhance the recommendation effectiveness, we propose to leverage multiple types of side information using the graph pre-training technique. In particular, we aim to generalise the pre-training technique used by SGP for multiple types of side information associated with both users and items. Specifically, we propose two novel pre-training schemes, namely Single-P and Multi-P, to leverage side information such as the ages and occupations of users and the textual reviews and categories of items. Instead of jointly training with two objectives, our pre-training schemes first pre-train a representation model under the users and items’ multi/single relational graphs constructed by their side information and then fine-tune their embeddings under an existing general representation-based recommendation model. Extensive experiments on three public datasets show that the graph pre-training technique can effectively enhance the effectiveness of ranking-based recommender systems and alleviates the cold-start problem. In addition, our pre-training schemes can provide more ef-fective initialisations for both the users and items; hence the robustness of fine-tuning models namely MF, NCF, NGCF and LightGCN, can be improved. Finally, to enhance the training efficiency of graph-based recommenders while ensuring their effectiveness, we propose to use the graph contrastive learning technique to improve the traditional random negative sampling approach. In particular, we propose a dynamic negative sampling (DNS) approach that leverages the graph contrastive learning technique to replace the randomly sampled negative items with more informative negative items. Our experiments show that DNS can improve the recommendation effectiveness of four competitive recommenders. Next, we further propose a novel graph-based model, i.e. MLP-CGRec, that leverages a multiple sampling approach to enhance the training efficiency of the graph-based recommender system. In particular, MLP-CGRec uses DNS to sample contrastive negative items and an efficient graph-based sampling method to select pseudo-positive samples. Experimental results on three public datasets show that MLP-CGRec can maintain competitive effectiveness and achieve the best efficiency compared with state-of-the-art recommender systems