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Edited by: Editorial Board of Journal of Data Acquisition and Processing
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  • Table of Content
      05 July 2018, Volume 33 Issue 4   
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    Special Section on Recommender Systems with Big Data
    Xiaofang Zhou, Hongzhi Yin
    Journal of Data Acquisition and Processing, 2018, 33 (4): 621-624. 
    Abstract   PDF(95KB) ( 188 )  
    A Survey on Expert Recommendation in Community Question Answering
    Xianzhi Wang, Chaoran Huang, Lina Yao, Boualem Benatallah, Manqing Dong
    Journal of Data Acquisition and Processing, 2018, 33 (4): 625-653. 
    Abstract   PDF(472KB) ( 881 )  
    Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time; on the other hand, an answerer might face numerous new questions without being able to identify the questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions.
    Jointly Recommending Library Books and Predicting Academic Performance: A Mutual Reinforcement Perspective
    De-Fu Lian, Qi Liu
    Journal of Data Acquisition and Processing, 2018, 33 (4): 654-667. 
    Abstract   PDF(1490KB) ( 612 )  
    The prediction of academic performance is one of the most important tasks in educational data mining, and has been widely studied in massive open online courses (MOOCs) and intelligent tutoring systems. Academic performance can be affected by factors like personality, skills, social environment, and the use of library books. However, it is still less investigated about how the use of library books can affect the academic performance of college students and even leverage book-loan history for predicting academic performance. To this end, we propose a supervised content-aware matrix factorization for mutual reinforcement of academic performance prediction and library book recommendation. This model not only addresses the sparsity challenge by explainable dimension reduction techniques, but also quantifies the importance of library books in predicting academic performance. Finally, we evaluate the proposed model on three consecutive years of book-loan history and cumulative grade point average of 13 047 undergraduate students in one university. The results show that the proposed model outperforms the competing baselines on both tasks, and that academic performance not only is predictable from the book-loan history but also improves the recommendation of library books for students.
    Multiple Auxiliary Information Based Deep Model for Collaborative Filtering
    Lin Yue, Xiao-Xin Sun, Wen-Zhu Gao, Guo-Zhong Feng, Bang-Zuo Zhang
    Journal of Data Acquisition and Processing, 2018, 33 (4): 668-681. 
    Abstract   PDF(2184KB) ( 621 )  
    With the ever-growing dynamicity, complexity, and volume of information resources, the recommendation technique is proposed and becomes one of the most effective techniques for solving the so-called problem of information overload. Traditional recommendation algorithms, such as collaborative filtering based on the user or item, only measure the degree of similarity between users or items with single criterion, i.e., ratings. According to the experience of previous studies, single criterion cannot accurately measure the similarity between user preferences or items. In recent years, the application of deep learning techniques has gained significant momentum in recommender systems for better understanding of user preferences, item characteristics, and historical interactions. In this work, we integrate plot information as auxiliary information into the denoising autoencoder (DAE), called SemRe-DCF, which aims at learning semantic representations of item descriptions and succeeds in capturing fine-grained semantic regularities by using vector arithmetic to get better rating prediction. The results manifest that the proposed method can effectively improve the accuracy of prediction and solve the cold start problem.
    Exploiting Pre-Trained Network Embeddings for Recommendations in Social Networks
    Lei Guo, Yu-Fei Wen, Xin-Hua Wang
    Journal of Data Acquisition and Processing, 2018, 33 (4): 682-696. 
    Abstract   PDF(5765KB) ( 544 )  
    Recommender systems as one of the most efficient information filtering techniques have been widely studied in recent years. However, traditional recommender systems only utilize user-item rating matrix for recommendations, and the social connections and item sequential patterns are ignored. But in our real life, we always turn to our friends for recommendations, and often select the items that have similar sequential patterns. In order to overcome these challenges, many studies have taken social connections and sequential information into account to enhance recommender systems. Although these existing studies have achieved good results, most of them regard social influence and sequential information as regularization terms, and the deep structure hidden in social networks and rating patterns has not been fully explored. On the other hand, neural network based embedding methods have shown their power in many recommendation tasks with their ability to extract high-level representations from raw data. Motivated by the above observations, we take the advantage of network embedding techniques and propose an embedding-based recommendation method, which is composed of the embedding model and the collaborative filtering model. Specifically, to exploit the deep structure hidden in social networks and rating patterns, a neural network based embedding model is first pre-trained, where the external user and item representations are extracted. Then, we incorporate these extracted factors into a collaborative filtering model by fusing them with latent factors linearly, where our method not only can leverage the external information to enhance recommendation, but also can exploit the advantage of collaborative filtering techniques. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed method and the importance of these external extracted factors.
    Discovering Functional Organized Point of Interest Groups for Spatial Keyword Recommendation
    Yan-Xia Xu, Wei Chen, Jia-Jie Xu, Zhi-Xu Li, Guan-Feng Liu, Lei Zhao
    Journal of Data Acquisition and Processing, 2018, 33 (4): 697-710. 
    Abstract   PDF(882KB) ( 600 )  
    A point of interest (POI) is a specific point location that someone may find useful. With the development of urban modernization, a large number of functional organized POI groups (FOPGs), such as shopping malls, electronic malls, and snacks streets, are springing up in the city. They have a great influence on people's lives. We aim to discover functional organized POI groups for spatial keyword recommendation because FOPGs-based recommendation is superior to POIs-based recommendation in efficiency and flexibility. To discover FOPGs, we design clustering algorithms to obtain organized POI groups (OPGs) and utilize OPGs-LDA (Latent Dirichlet Allocation) model to reveal functions of OPGs for further recommendation. To the best of our knowledge, we are the first to study functional organized POI groups which have important applications in urban planning and social marketing.
    Hashtag Recommendation Based on Multi-Features of Microblogs
    Fei-Fei Kou, Jun-Ping Du, Cong-Xian Yang, Yan-Song Shi, Wan-Qiu Cui, Mei-Yu Liang, Yue Geng
    Journal of Data Acquisition and Processing, 2018, 33 (4): 711-726. 
    Abstract   PDF(1035KB) ( 860 )  
    Hashtag recommendation for microblogs is a very hot research topic that is useful to many applications involving microblogs. However, since short text in microblogs and low utilization rate of hashtags will lead to the data sparsity problem, it is difficult for typical hashtag recommendation methods to achieve accurate recommendation. In light of this, we propose HRMF, a hashtag recommendation method based on multi-features of microblogs in this article. First, our HRMF expands short text into long text, and then it simultaneously models multi-features (i.e., user, hashtag, text) of microblogs by designing a new topic model. To further alleviate the data sparsity problem, HRMF exploits hashtags of both similar users and similar microblogs as the candidate hashtags. In particular, to find similar users, HRMF combines the designed topic model with typical user-based collaborative filtering method. Finally, we realize hashtag recommendation by calculating the recommended score of each hashtag based on the generated topical representations of multi-features. Experimental results on a real-world dataset crawled from Sina Weibo demonstrate the effectiveness of our HRMF for hashtag recommendation.
    A Generative Model Approach for Geo-Social Group Recommendation
    Peng-Peng Zhao, Hai-Feng Zhu, Yanchi Liu, Zi-Ting Zhou, Zhi-Xu Li, Jia-Jie Xu, Lei Zhao, Victor S. Sheng
    Journal of Data Acquisition and Processing, 2018, 33 (4): 727-738. 
    Abstract   PDF(719KB) ( 763 )  
    With the development and prevalence of online social networks, there is an obvious tendency that people are willing to attend and share group activities with friends or acquaintances. This motivates the study on group recommendation, which aims to meet the needs of a group of users, instead of only individual users. However, how to aggregate different preferences of different group members is still a challenging problem:1) the choice of a member in a group is influenced by various factors, e.g., personal preference, group topic, and social relationship; 2) users have different influences when in different groups. In this paper, we propose a generative geo-social group recommendation model (GSGR) to recommend points of interest (POIs) for groups. Specifically, GSGR well models the personal preference impacted by geographical information, group topics, and social influence for recommendation. Moreover, when making recommendations, GSGR aggregates the preferences of group members with different weights to estimate the preference score of a group to a POI. Experimental results on two datasets show that GSGR is effective in group recommendation and outperforms the state-of-the-art methods.
    Illuminating Recommendation by Understanding the Explicit Item Relations
    Qi Liu, Hong-Ke Zhao, Le Wu, Zhi Li, En-Hong Chen
    Journal of Data Acquisition and Processing, 2018, 33 (4): 739-755. 
    Abstract   PDF(1380KB) ( 407 )  
    Recent years have witnessed the prevalence of recommender systems in various fields, which provide a personalized recommendation list for each user based on various kinds of information. For quite a long time, most researchers have been pursing recommendation performances with predefined metrics, e.g., accuracy. However, in real-world applications, users select items from a huge item list by considering their internal personalized demand and external constraints. Thus, we argue that explicitly modeling the complex relations among items under domain-specific applications is an indispensable part for enhancing the recommendations. Actually, in this area, researchers have done some work to understand the item relations gradually from "implicit" to "explicit" views when recommending. To this end, in this paper, we conduct a survey of these recent advances on recommender systems from the perspective of the explicit item relation understanding. We organize these relevant studies from three types of item relations, i.e., combination-effect relations, sequence-dependence relations, and external-constraint relations. Specifically, the combination-effect relation and the sequence-dependence relation based work models the intra-group intrinsic relations of items from the user demand perspective, and the external-constraint relation emphasizes the external requirements for items. After that, we also propose our opinions on the open issues along the line of understanding item relations and suggest some future research directions in recommendation area.
    PTM: A Topic Model for the Inferring of the Penalty
    Tie-Ke He, Hao Lian, Ze-Min Qin, Zhen-Yu Chen, Bin Luo
    Journal of Data Acquisition and Processing, 2018, 33 (4): 756-767. 
    Abstract   PDF(388KB) ( 4966 )  
    Deciding the penalty of a law case has always been a complex process, which may involve with much coordination. Despite the judicial study based on the rules and conditions, artificial intelligence and machine learning has rarely been used to study the problem of penalty inferring, leaving the large amount of law cases as well as various factors among them untouched. This paper aims to incorporate the state-of-the-art artificial intelligence methods to exploit to what extent this problem can be alleviated. We first analyze 145 000 law cases and observe that there are two sorts of labels, temporal labels and spatial labels, which have unique characteristics. Temporal labels and spatial labels tend to converge towards the final penalty, on condition that the cases are of the same category. In light of this, we propose a latent-class probabilistic generative model, namely Penalty Topic Model (PTM), to infer the topic of law cases, and the temporal and spatial patterns of topics embedded in the case judgment. Then, the learnt knowledge is utilized to automatically cluster all cases accordingly in a unified way. We conduct extensive experiments to evaluate the performance of the proposed PTM on a real large-scale dataset of law cases. The experimental results show the superiority of our proposed PTM.
    Computer Networks and Distributed Computing
    A Survey on Task and Participant Matching in Mobile Crowd Sensing
    Yue-Yue Chen, Pin Lv, De-Ke Guo, Tong-Qing Zhou, Ming Xu
    Journal of Data Acquisition and Processing, 2018, 33 (4): 768-791. 
    Abstract   PDF(1201KB) ( 791 )  
    Mobile crowd sensing is an innovative paradigm which leverages the crowd, i.e., a large group of people with their mobile devices, to sense various information in the physical world. With the help of sensed information, many tasks can be fulfilled in an efficient manner, such as environment monitoring, traffic prediction, and indoor localization. Task and participant matching is an important issue in mobile crowd sensing, because it determines the quality and efficiency of a mobile crowd sensing task. Hence, numerous matching strategies have been proposed in recent research work. This survey aims to provide an up-to-date view on this topic. We propose a research framework for the matching problem in this paper, including participant model, task model, and solution design. The participant model is made up of three kinds of participant characters, i.e., attributes, requirements, and supplements. The task models are separated according to application backgrounds and objective functions. Offline and online solutions in recent literatures are both discussed. Some open issues are introduced, including matching strategy for heterogeneous tasks, context-aware matching, online strategy, and leveraging historical data to finish new tasks.
    Complete Your Mobility: Linking Trajectories Across Heterogeneous Mobility Data Sources
    Guo-Wei Wang, Jin-Dou Zhang, Jing Li
    Journal of Data Acquisition and Processing, 2018, 33 (4): 792-806. 
    Abstract   PDF(415KB) ( 452 )  
    Nowadays, human activities and movements are recorded by a variety of tools, forming different trajectory sets which are usually isolated from one another. Thus, it is very important to link different trajectories of one person in different sets to provide massive information for facilitating trajectory mining tasks. Most prior work took advantages of only one dimensional information to link trajectories and can link trajectories in a one-to-many manner (providing several candidate trajectories to link to one specific trajectory). In this paper, we propose a novel approach called one-to-one constraint trajectory linking with multi-dimensional information (OCTL) that links the corresponding trajectories of one person in different sets in a one-to-one manner. We extract multidimensional features from different trajectory datasets for corresponding relationships prediction, including spatial, temporal and spatio-temporal information, which jointly describe the relationships between trajectories. Using these features, we calculate the corresponding probabilities between trajectories in different datasets. Then, we formulate the link inference problem as a bipartite graph matching problem and employ effective methods to link one trajectory to another. Moreover, the advantages of our approach are empirically verified on two real-world trajectory sets with convincing results.
    Artificial Intelligence and Pattern Recognition
    Hierarchical Clustering of Complex Symbolic Data and Application for Emitter Identification
    Xin Xu, Jiaheng Lu, Wei Wang
    Journal of Data Acquisition and Processing, 2018, 33 (4): 807-822. 
    Abstract   PDF(469KB) ( 464 )  
    It is well-known that the values of symbolic variables may take various forms such as an interval, a set of stochastic measurements of some underlying patterns or qualitative multi-values and so on. However, the majority of existing work in symbolic data analysis still focuses on interval values. Although some pioneering work in stochastic pattern based symbolic data and mixture of symbolic variables has been explored, it still lacks flexibility and computation efficiency to make full use of the distinctive individual symbolic variables. Therefore, we bring forward a novel hierarchical clustering method with weighted general Jaccard distance and effective global pruning strategy for complex symbolic data and apply it to emitter identification. Extensive experiments indicate that our method has outperformed its peers in both computational efficiency and emitter identification accuracy.
    A Two-Player Coalition Cooperative Scheme for the Bodyguard Allocation Problem
    José Alberto Fernández-Zepeda, Daniel Brubeck-Salcedo, Daniel Fajardo-Delgado, Héctor Zatarain-Aceves
    Journal of Data Acquisition and Processing, 2018, 33 (4): 823-837. 
    Abstract   PDF(1520KB) ( 2470 )  
    We address the bodyguard allocation problem (BAP), an optimization problem that illustrates the conflict of interest between two classes of processes with contradictory preferences within a distributed system. While a class of processes prefers to minimize its distance to a particular process called the root, the other class prefers to maximize it; at the same time, all the processes seek to build a communication spanning tree with the maximum social welfare. The two state-of-the-art algorithms for this problem always guarantee the generation of a spanning tree that satisfies a condition of Nash equilibrium in the system; however, such a tree does not necessarily produce the maximum social welfare. In this paper, we propose a two-player coalition cooperative scheme for BAP, which allows some processes to perturb or break a Nash equilibrium to find another one with a better social welfare. By using this cooperative scheme, we propose a new algorithm called FFC-BAPS for BAP. We present both theoretical and empirical analyses which show that this algorithm produces better quality approximate solutions than former algorithms for BAP.
    3D Filtering by Block Matching and Convolutional Neural Network for Image Denoising
    Bei-Ji Zou, Yun-Di Guo, Qi He, Ping-Bo Ouyang, Ke Liu, Zai-Liang Chen
    Journal of Data Acquisition and Processing, 2018, 33 (4): 838-848. 
    Abstract   PDF(6419KB) ( 1212 )  
    Block matching based 3D filtering methods have achieved great success in image denoising tasks. However, the manually set filtering operation could not well describe a good model to transform noisy images to clean images. In this paper, we introduce convolutional neural network (CNN) for the 3D filtering step to learn a well fitted model for denoising. With a trainable model, prior knowledge is utilized for better mapping from noisy images to clean images. This block matching and CNN joint model (BMCNN) could denoise images with different sizes and different noise intensity well, especially images with high noise levels. The experimental results demonstrate that among all competing methods, this method achieves the highest peak signal to noise ratio (PSNR) when denoising images with high noise levels (σ > 40), and the best visual quality when denoising images with all the tested noise levels.
    Regular Paper
    Mining Semantic Trajectory Patterns from Geo-Tagged Data
    Guochen Cai, Kyungmi Lee, Ickjai Lee
    Journal of Data Acquisition and Processing, 2018, 33 (4): 849-862. 
    Abstract   PDF(1852KB) ( 560 )  
    User-generated social media data tagged with geographic information present messages of dynamic spatiotemporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge.
    A Gradient-Domain Based Geometry Processing Framework for Point Clouds
    Hong-Xing Qin, Jin-Long He, Meng-Hui Wang, Yu Dai, Zhi-Yong Ran
    Journal of Data Acquisition and Processing, 2018, 33 (4): 863-872. 
    Abstract   PDF(3121KB) ( 361 )  
    The use of point clouds is becoming increasingly popular. We present a general framework for performing geometry filtering on point-based surface through applying the meshless local Petrol-Galelkin (MLPG) to obtain the solution of a screened Poisson equation. The enhancement or smoothing of surfaces is controlled by a gradient scale parameter. Anisotropic filtering is supported by the adapted Riemannian metric. Contrary to the other approaches of partial differential equation for point-based surface, the proposed approach neither needs to construct local or global triangular meshes, nor needs global parameterization. It is only based on the local tangent space and local interpolated surfaces. Experiments demonstrate the efficiency of our approach.
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ISSN 1004-9037


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