Jibing Gong's research while affiliated with Northeast University At Qinhuangdao Campus and other places

Publications (40)

Preprint
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Massive open online courses (MOOCs), which provide a large-scale interactive participation and open access via the web, are becoming a modish way for online and distance education. To help users have a better study experience, many MOOC platforms have provided the services of recommending courses to users. However, we argue that directly recommendi...
Article
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Predicting users’ next behavior through learning users’ preferences according to the users’ historical behaviors is known as sequential recommendation. In this task, learning sequence representation by modeling the pairwise relationship between items in the sequence to capture their long-range dependencies is crucial. In this paper, we propose a no...
Article
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To provide more accurate and stable recommendations, it is necessary to combine display information with implicit information and to dig out potential information. Existing methods only consider explicit feedback information or implicit feedback information unilaterally and ignore the potential information of explicit feedback information and impli...
Article
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In MOOCs, generally speaking, curriculum designing, course selection, and knowledge concept recommendation are the three major steps that systematically instruct users to learn. This paper focuses on the knowledge concept recommendation in MOOCs, which recommends related topics to users to facilitate their online study. The existing approaches only...
Article
We propose a self-supervised embedding learning frameworkSelfLinKGto link concepts in heterogeneous knowledge graphs. Without any labeled data, SelfLinKG can achieve competitive performance against its supervised counterpart, and significantly outperforms state-of-the-art unsupervised methods by 26%-50%. The essential components of SelfLinKG are lo...
Article
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Personality characteristics represent the behavioral characteristics of a class of people. Social networking sites have a multitude of users, and the text messages generated by them convey a person’s feelings, thoughts, and emotions at a particular time. These social texts indeed record the long-term psychological activities of users, which can be...
Article
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In this era of exponential growth in the scale of data, information overload has become an urgent problem, and the use of increasingly flexible sensor cloud systems (SCS) for data collection has become a mainstream trend. Recommendation algorithms can search massive data sets to uncover information that meets the needs of users based on their inter...
Preprint
Full-text available
Massive open online courses are becoming a modish way for education, which provides a large-scale and open-access learning opportunity for students to grasp the knowledge. To attract students' interest, the recommendation system is applied by MOOCs providers to recommend courses to students. However, as a course usually consists of a number of vide...
Article
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In recent years, the number of fraud cases in basic medical insurance has increased dramatically. We need to use a more efficient method to identify the fraudulent users. Therefore, we deploy the cloud edge algorithm with lower latency to improve the security and enforceability in the operation process. In this paper, a new feature extraction metho...
Article
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Traditional methods of multi-label text classification, particularly deep learning, have achieved remarkable results. However, most of these methods use word2vec technology to represent sequential text information, while ignoring the logic and internal hierarchy of the text itself. Although these approaches can learn the hypothetical hierarchy and...
Article
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Usually, in addition to the main content, web pages contain additional information in the form of noise, such as navigation elements, sidebars and advertisements. This kind of noise has nothing to do with the main content, it will affect the tasks of data mining and information retrieval so that the sensor will be damaged by the wrong data and inte...
Article
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With the rising popularity of social networks and service recommendations, research on new methods of friend recommendation have become a key topic, especially when based on quality-driven resource processing in an edge computing environment. Traditional methods seldom systematically combine static attributes (e.g., interests, geographical location...
Chapter
User-based attribute information, such as age and gender, is usually considered as user privacy information. It is difficult for enterprises to obtain user-based privacy attribute information. However, user-based privacy attribute information has a wide range of applications in personalized services, user behavior analysis and other aspects. Althou...
Preprint
User-based attribute information, such as age and gender, is usually considered as user privacy information. It is difficult for enterprises to obtain user-based privacy attribute information. However, user-based privacy attribute information has a wide range of applications in personalized services, user behavior analysis and other aspects. this p...
Preprint
Most of the information is stored as text, so text mining is regarded as having high commercial potential. Aiming at the semantic constraint problem of classification methods based on sparse representation, we propose a weighted recurrent neural network (W-RNN), which can fully extract text serialization semantic information. For the problem that t...
Article
Community phenomenon is ubiquitous in our social activities. For instance, in a football match, the players are divided into two disjoint teams (i.e., communities) in which the ball is frequently forwarded from one player to another, generating many ball transferring trajectories. It is interesting to do a community detection which is only based on...
Article
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Timely discovering and acquiring information from incremental data on the Internet is a hot topic in a big data era. This paper presents a distributed incremental information acquisition model for large-scale text data. To obtain a lower false positive rate and higher efficiency of the traditional Bloom filter, a distributed multidimensional Bloom...
Chapter
Friend recommendation is a fundamental service for both social networks and practical applications. The majority of existing friend-recommendation methods utilize user profiles, social relationships, or static post content data, but rarely consider the semantic intentions and dynamic behaviors of users. In this paper, we propose FRec++, a friend re...
Article
Friend recommendation is a fundamental service in both social networks and practical applications, and is influenced by user behaviors such as interactions, interests, and activities. In this study, we first conduct in-depth investigations on factors that affect recommendation results. Next, we design Friend++, a hybrid multi-individual recommendat...
Conference Paper
In social networks, current friend/user recommendation methods are mainly based on similarity measurements among users or the structure of social networks. In this paper, we design a novel friend recommendation method according to a new individual feature intimacy degree. Intimacy degree reflects the degree of interaction between two users and furt...
Article
In this paper, the authors try to systematically investigate the problem of individual doctor recommendation and propose a novel method to enable patients to access such intelligent medical service. In their method, the authors first mine doctor-patient ties/relationships via Time-constraint Probability Factor Graph model (TPFG) from a medical soci...
Article
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With the rapid growth of spatial data, traditional cause-effect analysis and conditional retrieval fall short in the era of big data. Associative retrieval is more reasonable and feasible. To promote the associative retrieval of spatial big data, this paper investigates the combination of the spreading activation (SA) algorithm and spatial ontology...
Conference Paper
In this paper, we try to systematically study how to perform doctor recommendation in mobile Medical Social Networks (m-MSNs). Specifically, employing a real-world medical dataset as the source in our study, we first mine doctor-patient ties/relationships via Time-constraint Probability Factor Graph model (TPFG), and then define the transition prob...
Article
A current trend for online social networks is to turn mobile. Mobile social networks directly reflect our real social life, and therefore are an important source to analyze and understand the underlying dynamics of human behaviors (activities). In this paper, we study the problem of activity prediction in mobile social networks. We present a series...
Conference Paper
Social interest refers to a kind of preference that an individual enjoys in social networks. In real world, people's social interests are influenced by various factors such as social relationships, historic social interests and users' private attributes. However, few publications systematically study the problem of social interest inferring in a re...
Article
Full-text available
We first propose an MPD-Model, a novel distributed multipreference-driven data fusion model for WSNs. Here, preferences are looked as the core elements of collaboration mechanism in a data fusion procedure. We then present MFA, a distributed multi-preference feature-level fusion algorithm based on weighted average method. Next, to implement feature...
Conference Paper
It is difficult for patients to find the most appropriate doctor/physician to diagnose. In most cases, just considering Authority Degrees of Candidate Doctors(AD-CDs) cannot satisfy this need due to some objective preferences such as economic affordability of a patient, commuting distance for visiting doctors and so on. In this paper, we try to sys...
Conference Paper
Full-text available
Traditional Chinese Pulse Diagnosis is a convenient and noninvasive method for disease diagnosis and healthcare. We have designed and implemented a Chinese wrist-pulse retrieval system based on the principle of Traditional Chinese Pulse Diagnosis (TCPD), called EasiCPRS. It is designed to be small in size, low in cost, with flexibility in deploymen...
Conference Paper
Pulse Diagnosis Theory (PDT) has the advantages of non-invasive treatment and disease prevention. Combining these merits with Wireless Sensor Network (WSN), we propose a novel networked low-cost and wearable healthcare monitoring system, namely PDhms, for pulse data collection, pulse analysis and pulse diagnosis. Some practical challenges still exi...
Article
Traffic information acquisition is often implemented by video cameras or inductive loops, which is expensive or inconvenient from installation and maintenance perspectives. We designed and implemented a pervasive traffic information acquisition system based on wireless sensor networks called EasiTia. Unlike existing solutions, the implementation of...
Conference Paper
Wireless Sensor Networks (WSN) with small, densely distributed wireless sensor nodes are being envisioned and developed for a variety of applications. The advantages of WSN enable its significant prospects in target identification. Vehicle identification is the basic issue in Intelligent Traffic System (ITS) which is the typical application of WSN....
Conference Paper
To meet the increasing needs of home-based healthcare, we propose a novel networked low-cost and wearable healthcare monitoring system, namely TCM-PCA, for pulse analysis in Traditional Chinese Medicine(TCM). The unique innovations of the system are as follows: (1) We propose a novel adaptive features extraction algorithm for pulse waveform to impr...
Conference Paper
Full-text available
In our economic society, future stock price trend is very hot focus that the investors concern about. Challenges still exist in stock price prediction model regarding significant time-effectiveness of prediction, the complexity of methods and selection of feature index variables. In this paper, we present a new approach based on Logistic Regression...
Conference Paper
The problem of Attribute Value Rough Equality (AVRE) is a fundamental problem in the fields of Text Classification and Information Retrieval. However,challenge still exists. In practical application, the processed attribute values are often data/information set based on semantics. This situation is very difficult to be handled by the traditional th...

Citations

... For this reason, we considered that the developed approach should include stream mining logic [7]. For this purpose, as the BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm is one of the most prominent approaches in the literature [8,9], which has been used in up-to-date studies [10,11], we also focused on it in our study and experiments. Thanks to the balanced CF-tree (Cluster Features) data structure obtained, the neighbor job postings of a new job posting can be discovered quickly, and based on the texts of these neighboring job postings, new qualifications may be rapidly obtained to make recommendations for new job postings [12]. ...
... There is no interactive communication between the exhibits and the audience, which makes the viewing efficiency ineffective and the purpose of education promotion is weak. The emergence of digital art display forms has effectively made up for the defects of traditional museum display design [11][12][13]. Digital art is a modern interactive medium, which can make the audience feel immersive and has a strong sense of immersion and interaction. ...
... The idea is to use sensor data from mobile phone users to mine potential friends who are similar to the target user through contextual information, so that the recommendation results more accurately reflect user preferences, as online comments will have an impact on consumers' purchase behavior. Gong et al. [24] explored the potential of using explicit and implicit information to mine the potential information of the meta-path in recommendation. Ma et al. [25] proposed a recommendation model that comprehensively considers local information and global information. ...
... However, most embedding-based methods nowadays rely heavily on supervised data, hindering their application in real web-scale noisy data. As a prior effort, in [22] authors present self-supervised pre-training for concept linking but with downstream supervised classification. In this work, we endeavor to investigate the potential of a completely self-supervised approach without using labels to reduce the cost of entity alignment while improving performance. ...
... However, when the algorithm is applied to a large data set, it will lead to overfitting and reduce the accuracy to about 55%. Wang et al. [17] proposed a deep learning framework that combines XLNet with the Personality Classification Capsule Network (XLNet-Caps) from text posts and found that personality could be effectively classified, and the average recall rate could reach 77.1%, but the value of F1 was only 68%. ...
... Many experiments have shown that deep neural networks (DNNs) are used in several fields because of their ability to capture complex and deeper information, including image segmentation [1], natural language processing [2,3], speech recognition [4], and recommendation systems [5][6][7]. Dailing Zhang et al. [8] designed deep learning-based frameworks that consist of both convolutional and recurrent neural networks to precisely identify human intentions in brain-computer interfaces. Kaixuan Chen et al. [9] proposed a semisupervised deep model for imbalanced activity recognition and pattern-balanced cotraining for extracting and preserving the latent activity patterns to improve the robustness of co-training on imbalanced data. ...
... Graph neural networks [20] have become a research hotspot in the field of deep learning. According to the number of entity types contained in the graph, it can be divided into homogeneous graphs [21] and heterogeneous graphs [22]. Wang et al. [23] proposed a heterogeneous graph neural network based on node-level and semantic-level attention mechanism. ...
... Kaelbling points to the over-reliance on the System1/System2 analogy, and advocates for a much more diverse and dynamic approach. We posit that the type of reasoning employed should [112,120,123,145] 0 n n n n n n not be based solely on how we think people think, but on the attendant objective. This is in line with the "goal oriented" theory from neuroscience, in that reasoning involves many sub-systems: perception, information retrieval, decision making, planning, controlling, and executing, utilizing working memory, calculation, and pragmatics. ...
... In this situation, many machine learning and deep learning models are applied to recommendation systems. The authors in [14] design a hybrid deep neural network which combines attribute attention and network embedding to make recommendation with the help of both interactive semantics and contextual enhancement. Article [15] proposes a matrix factorization model with deep features learning which integrates a convolutional neural network. ...
... There are lots of previous work in the field of detecting communities or user subgroups in social networks [16], [17], [18], [19] and human mobility data [20], [21], [22], where the main approaches are based on comparing and clustering the properties and characteristics of the contents, the network structure, or a mixture of both. To detect communities based on such data, the features characterizing each individual need to be extracted and data clustering algorithms should be properly designed to group these individuals to the communities they belong to. ...