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Zhang P, Liu B, Lu T, Ding X, Gu H, Gu N. Jointly Predicting Future Content in Multiple Social Media Sites Based on Multi-task Learning. ACM T INFORM SYST 2022. [DOI: 10.1145/3495530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
User-generated contents (UGC) in social media are the direct expression of users’ interests, preferences, and opinions. User behavior prediction based on UGC has increasingly been investigated in recent years. Compared to learning a person’s behavioral patterns in each social media site separately, jointly predicting user behavior in multiple social media sites and complementing each other (cross-site user behavior prediction) can be more accurate. However, cross-site user behavior prediction based on UGC is a challenging task due to the difficulty of cross-site data sampling, the complexity of UGC modeling, and uncertainty of knowledge sharing among different sites. For these problems, we propose a Cross-Site Multi-Task (CSMT) learning method to jointly predict user behavior in multiple social media sites. CSMT mainly derives from the hierarchical attention network and multi-task learning. Using this method, the UGC in each social media site can obtain fine-grained representations in terms of words, topics, posts, hashtags, and time slices as well as the relevances among them, and prediction tasks in different social media sites can be jointly implemented and complement each other. By utilizing two cross-site datasets sampled from Weibo, Douban, Facebook, and Twitter, we validate our method’s superiority on several classification metrics compared with existing related methods.
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Affiliation(s)
- Peng Zhang
- School of Computer Science, Fudan University, Shanghai, China
| | - Baoxi Liu
- School of Computer Science, Fudan University, Shanghai, China
| | - Tun Lu
- School of Computer Science, Fudan University, Shanghai, China
| | - Xianghua Ding
- School of Computer Science, Fudan University, Shanghai, China
| | | | - Ning Gu
- School of Computer Science, Fudan University, Shanghai, China
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2
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Zhong ST, Huang L, Wang CD, Lai JH, Yu PS. An Autoencoder Framework With Attention Mechanism for Cross-Domain Recommendation. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:5229-5241. [PMID: 33156800 DOI: 10.1109/tcyb.2020.3029002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In recent years, the recommender system has been widely used in online platforms, which can extract useful information from giant volumes of data and recommend suitable items to the user according to user preferences. However, the recommender system usually suffers from sparsity and cold-start problems. Cross-domain recommendation, as a particular example of transfer learning, has been used to solve the aforementioned problems. However, many existing cross-domain recommendation approaches are based on matrix factorization, which can only learn the shallow and linear characteristics of users and items. Therefore, in this article, we propose a novel autoencoder framework with an attention mechanism (AAM) for cross-domain recommendation, which can transfer and fuse information between different domains and make a more accurate rating prediction. The main idea of the proposed framework lies in utilizing autoencoder, multilayer perceptron, and self-attention to extract user and item features, learn the user and item-latent factors, and fuse the user-latent factors from different domains, respectively. In addition, to learn the affinity of the user-latent factors between different domains in a multiaspect level, we also strengthen the self-attention mechanism by using multihead self-attention and propose AAM++. Experiments conducted on two real-world datasets empirically demonstrate that our proposed methods outperform the state-of-the-art methods in cross-domain recommendation and AAM++ performs better than AAM on sparse and large-scale datasets.
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3
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Tu HT, Phan TT, Nguyen KP. Modeling information diffusion in social networks with ordinary linear differential equations. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.01.063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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4
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Hu C, Yin M, Liu B, Li X, Ye Y. Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion. ACM T INTEL SYST TEC 2021. [DOI: 10.1145/3472713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Illicit drug trafficking via social media sites such as Instagram have become a severe problem, thus drawing a great deal of attention from law enforcement and public health agencies. How to identify illicit drug dealers from social media data has remained a technical challenge for the following reasons. On the one hand, the available data are limited because of privacy concerns with crawling social media sites; on the other hand, the diversity of drug dealing patterns makes it difficult to reliably distinguish drug dealers from common drug users. Unlike existing methods that focus on posting-based detection, we propose to tackle the problem of
illicit drug dealer identification
by constructing a large-scale multimodal dataset named
Identifying Drug Dealers on Instagram
(IDDIG). Nearly 4,000 user accounts, of which more than 1,400 are drug dealers, have been collected from Instagram with multiple data sources including post comments, post images, homepage bio, and homepage images. We then design a quadruple-based multimodal fusion method to combine the multiple data sources associated with each user account for drug dealer identification. Experimental results on the constructed IDDIG dataset demonstrate the effectiveness of the proposed method in identifying drug dealers (almost 95% accuracy). Moreover, we have developed a hashtag-based community detection technique for discovering evolving patterns, especially those related to geography and drug types.
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Affiliation(s)
- Chuanbo Hu
- West Virginia University, Morgantown, WV
| | | | - Bin Liu
- West Virginia University, Morgantown, WV
| | - Xin Li
- West Virginia University, Morgantown, WV
| | - Yanfang Ye
- Case Western Reserve University, Cleveland, OH
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5
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Xu LP, Liao JB, Wu YS, da Kuang H. Effect of Psychological Capital of Volunteers on Volunteering Behavior: The Chained Mediation Role of Perceived Social Support and Volunteer Motivation. Front Psychol 2021; 12:657877. [PMID: 34603118 PMCID: PMC8484802 DOI: 10.3389/fpsyg.2021.657877] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 08/05/2021] [Indexed: 11/29/2022] Open
Abstract
This study explored the role of perceived social support and voluntary motivation in the effect of psychological capital of volunteers on volunteering behavior. A sample of 1,165 volunteers who were registered in the China Voluntary Service Information System was investigated using a self-reported questionnaire, showing that the psychological capital, perceived social support, voluntary motivation, and volunteering behavior of the volunteers were significantly and positively related to each other. The psychological capital of the volunteers affected volunteering behavior not only directly, but also indirectly through the mediating role of voluntary motivation. Moreover, perceived social support and voluntary motivation also played a chain role in the relationship between the psychological capital and volunteering behavior of the volunteers. Therefore, increasing the psychological capital of the volunteers should promote their perceived social support and inspire voluntary motivation, in turn affecting their volunteering behavior.
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Affiliation(s)
- Li ping Xu
- Department of Social Science, Zhuhai of Zunyi Medical University, Zhuhai, China
| | - Jin bao Liao
- Guangdong Communication Polytechnic, Guangzhou, China
| | - Yu shen Wu
- Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Hong da Kuang
- School of Marxism, Guilin University of Electronic Technology, Guilin, China
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6
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Qin C, Zhu H, Xu T, Zhu C, Ma C, Chen E, Xiong H. An Enhanced Neural Network Approach to Person-Job Fit in Talent Recruitment. ACM T INFORM SYST 2020. [DOI: 10.1145/3376927] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
The widespread use of online recruitment services has led to an information explosion in the job market. As a result, recruiters have to seek intelligent ways for Person-Job Fit, which is the bridge for adapting the right candidates to the right positions. Existing studies on Person-Job Fit usually focus on measuring the matching degree between talent qualification and job requirements mainly based on the manual inspection of human resource experts, which could be easily misguided by the subjective, incomplete, and inefficient nature of human judgment. To that end, in this article, we propose a novel end-to-end
T
opic-based
A
bility-aware
P
erson-
J
ob
F
it
N
eural
N
etwork (TAPJFNN) framework, which has a goal of reducing the dependence on manual labor and can provide better interpretability about the fitting results. The key idea is to exploit the rich information available in abundant historical job application data. Specifically, we propose a word-level semantic representation for both job requirements and job seekers’ experiences based on Recurrent Neural Network (RNN). Along this line, two hierarchical topic-based ability-aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measure the different contribution of each job experience to a specific ability requirement. In addition, we design a refinement strategy for Person-Job Fit prediction based on historical recruitment records. Furthermore, we introduce how to exploit our TAPJFNN framework for enabling two specific applications in talent recruitment: talent sourcing and job recommendation. Particularly, in the application of job recommendation, a novel training mechanism is designed for addressing the challenge of biased negative labels. Finally, extensive experiments on a large-scale real-world dataset clearly validate the effectiveness and interpretability of the TAPJFNN and its variants compared with several baselines.
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Affiliation(s)
- Chuan Qin
- School of Computer Science, University of Science and Technology of China
| | | | - Tong Xu
- School of Computer Science, University of Science and Technology of China
| | - Chen Zhu
- Baidu Talent Intelligence Center, Baidu Inc
| | - Chao Ma
- Baidu Talent Intelligence Center, Baidu Inc
| | - Enhong Chen
- School of Computer Science, University of Science and Technology of China
| | - Hui Xiong
- School of Computer Science, University of Science and Technology of China
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7
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Li LL, Sun J, Wang CH, Zhou YT, Lin KP. Enhanced Gaussian process mixture model for short-term electric load forecasting. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2018.10.063] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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9
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Liu X, Xia Y, Yang W, Yang F. Secure and efficient querying over personal health records in cloud computing. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2016.06.100] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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10
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Mao XL, Hao YJ, Wang D, Huang H. Query completion in community-based Question Answering search. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2016.06.096] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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11
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Nie L, Zhang L, Yan Y, Chang X, Liu M, Shaoling L. Multiview Physician-Specific Attributes Fusion for Health Seeking. IEEE TRANSACTIONS ON CYBERNETICS 2017; 47:3680-3691. [PMID: 27337733 DOI: 10.1109/tcyb.2016.2577590] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Community-based health services have risen as important online resources for resolving users health concerns. Despite the value, the gap between what health seekers with specific health needs and what busy physicians with specific attitudes and expertise can offer is being widened. To bridge this gap, we present a question routing scheme that is able to connect health seekers to the right physicians. In this scheme, we first bridge the expertise matching gap via a probabilistic fusion of the physician-expertise distribution and the expertise-question distribution. The distributions are calculated by hypergraph-based learning and kernel density estimation. We then measure physicians attitudes toward answering general questions from the perspectives of activity, responsibility, reputation, and willingness. At last, we adaptively fuse the expertise modeling and attitude modeling by considering the personal needs of the health seekers. Extensive experiments have been conducted on a real-world dataset to validate our proposed scheme.
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12
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Zhou GY, Huang JX. Modeling and Mining Domain Shared Knowledge for Sentiment Analysis. ACM T INFORM SYST 2017. [DOI: 10.1145/3091995] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of user generated sentiment data (e.g., reviews, blogs). In real applications, these user-generated sentiment data can span so many different domains that it is difficult to label the training data for all of them. Therefore, we study the problem of sentiment classification adaptation task in this article. That is, a system is trained to label reviews from one source domain but is meant to be used on the target domain. One of the biggest challenges for sentiment classification adaptation task is how to deal with the problem when two data distributions between the source domain and target domain are significantly different from one another. However, our observation is that there might exist some domain shared knowledge among certain input dimensions of different domains. In this article, we present a novel method for modeling and mining the domain shared knowledge from different sentiment review domains via a joint non-negative matrix factorization–based framework. In this proposed framework, we attempt to learn the domain shared knowledge and the domain-specific information from different sentiment review domains with several various regularization constraints. The advantage of the proposed method can promote the correspondence under the topic space between the source domain and the target domain, which can significantly reduce the data distribution gap across two domains. We conduct extensive experiments on two real-world balanced data sets from Amazon product reviews for sentence-level and document-level binary sentiment classification. Experimental results show that our proposed approach significantly outperforms several strong baselines and achieves an accuracy that is competitive with the most well-known methods for sentiment classification adaptation.
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Affiliation(s)
| | - Jimmy Xiangji Huang
- Information Retrieval and Knowledge Management Research Lab, York University, Ontario, Canada
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13
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Abstract
Event-based social networking services, such as Meetup, are capable of linking online virtual interactions to offline physical activities. Compared to mono online social networking services (e.g., Twitter and Google+), such dual networks provide a complete picture of users’ online and offline behaviors that more often than not are compatible and complementary. In the light of this, we argue that joint learning over dual networks offers us a better way to comprehensively understand user behaviors and their underlying organizational principles. Despite its value, few efforts have been dedicated to jointly considering the following factors within a unified model: (1) local user contextualization, (2) global structure coherence, and (3) effectiveness evaluation. Toward this end, we propose a novel dual clustering model for community detection over dual networks to jointly model local consistency for a specific user and global consistency of partitioning results across networks. We theoretically derived its solution. In addition, we verified our model regarding multiple metrics from different aspects and applied it to the application of event attendance prediction.
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14
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Kim Y, Jung W, Shim K. Integration of graphs from different data sources using crowdsourcing. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2017.01.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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