Table of Contents

Citations to this Journal [529 citations: 1–100 of 528 articles]

Articles published in Advances in Artificial Intelligence have been cited 529 times. The following is a list of the 528 articles that have cited the articles published in Advances in Artificial Intelligence.

  • Zhijun Ding, Xiaolun Li, Changjun Jiang, and Mengchu Zhou, “Objectives and State-of-the-Art of Location-Based Social Network Recommender Systems,” ACM Computing Surveys, vol. 51, no. 1, pp. 1–28, 2018. View at Publisher · View at Google Scholar
  • Bilel Elayeb, “Arabic word sense disambiguation: a review,” Artificial Intelligence Review, 2018. View at Publisher · View at Google Scholar
  • Alfred Castillo, Debra Vander Meer, and Arturo Castellanos, “ExUP recommendations: Inferring user's product metadata preferences from single-criterion rating systems,” Decision Support Systems, 2018. View at Publisher · View at Google Scholar
  • Hyunwoo Hwangbo, Yang Sok Kim, and Kyung Jin Cha, “Recommendation system development for fashion retail e-commerce,” Electronic Commerce Research and Applications, vol. 28, pp. 94–101, 2018. View at Publisher · View at Google Scholar
  • Wook-Yeon Hwang, “Assessing New Correlation-Based Collaborative Filtering Approaches for Binary Market Basket Data,” Electronic Commerce Research and Applications, 2018. View at Publisher · View at Google Scholar
  • Nikolaos Polatidis, Elias Pimenidis, Michalis Pavlidis, Spyridon Papastergiou, and Haralambos Mouratidis, “From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks,” Evolving Systems, 2018. View at Publisher · View at Google Scholar
  • Hongtao Wang, Hongmei Wang, Feng Yi, Hui Wen, Gang Li, and Limin Sun, “Context-aware personalized path inference from large-scale GPS snippets,” Expert Systems with Applications, vol. 91, pp. 78–88, 2018. View at Publisher · View at Google Scholar
  • Mehrbakhsh Nilashi, Othman Ibrahim, and Karamollah Bagherifard, “A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques,” Expert Systems with Applications, vol. 92, pp. 507–520, 2018. View at Publisher · View at Google Scholar
  • Libo Zhang, Tiejian Luo, Fei Zhang, and Yanjun Wu, “A Recommendation Model Based on Deep Neural Network,” IEEE Access, vol. 6, pp. 9454–9463, 2018. View at Publisher · View at Google Scholar
  • Zhiwei Guo, Chaowei Tang, Hui Tang, Yunqing Fu, and Wenjia Niu, “A Novel Group Recommendation Mechanism From the Perspective of Preference Distribution,” IEEE Access, vol. 6, pp. 5865–5878, 2018. View at Publisher · View at Google Scholar
  • Nasser R. Sabar, Xun Yi, and Andy Song, “A Bi-objective Hyper-Heuristic Support Vector Machines for Big Data Cyber-Security,” IEEE Access, vol. 6, pp. 10421–10431, 2018. View at Publisher · View at Google Scholar
  • Tito Pradhono Tomo, Alexander Schmitz, Wai Keat Wong, Harris Kristanto, Sophon Somlor, Jinsun Hwang, Lorenzo Jamone, and Shigeki Sugano, “Covering a Robot Fingertip With uSkin: A Soft Electronic Skin With Distributed 3-Axis Force Sensitive Elements for Robot Hands,” IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 124–131, 2018. View at Publisher · View at Google Scholar
  • Shibin Parameswaran, Enming Luo, and Truong Q. Nguyen, “Patch Matching for Image Denoising Using Neighborhood-Based Collaborative Filtering,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 2, pp. 392–401, 2018. View at Publisher · View at Google Scholar
  • Xiaoying Tan, Yuchun Guo, Yishuai Chen, and Wei Zhu, “Improving Recommendation via Inference of User Popularity Preference in Sparse Data Environment,” IEICE Transactions on Information and Systems, vol. E101.D, no. 4, pp. 1088–1095, 2018. View at Publisher · View at Google Scholar
  • Sang-Chul Lee, Sang-Wook Kim, Sunju Park, and Dong-Kyu Chae, “An Approach to Effective Recommendation Considering User Preference and Diversity Simultaneously,” IEICE Transactions on Information and Systems, vol. E101.D, no. 1, pp. 244–248, 2018. View at Publisher · View at Google Scholar
  • Hamed Zamani, and Azadeh Shakery, “A language model-based framework for multi-publisher content-based recommender systems,” Information Retrieval Journal, 2018. View at Publisher · View at Google Scholar
  • Tadiparthi V.R. Himabindu, Vineet Padmanabhan, and Arun K. Pujari, “Conformal matrix factorization based recommender system,” Information Sciences, 2018. View at Publisher · View at Google Scholar
  • Federica Cena, Silvia Likavec, and Amon Rapp, “Real World User Model: Evolution of User Modeling Triggered by Advances in Wearable and Ubiquitous Computing,” Information Systems Frontiers, 2018. View at Publisher · View at Google Scholar
  • Xiaoyu Sun, Zhou Huang, Xia Peng, Yiran Chen, and Yu Liu, “Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data,” International Journal of Digital Earth, pp. 1–18, 2018. View at Publisher · View at Google Scholar
  • Benedikt Loepp, Tim Donkers, Timm Kleemann, and Jürgen Ziegler, “Interactive Recommending with Tag-Enhanced Matrix Factorization ( TagMF ),” International Journal of Human-Computer Studies, 2018. View at Publisher · View at Google Scholar
  • Xueying Ma, and Lu Ye, “Career Goal-based E-Learning Recommendation Using Enhanced Collaborative Filtering and PrefixSpan,” International Journal of Mobile and Blended Learning, vol. 10, no. 3, pp. 23–37, 2018. View at Publisher · View at Google Scholar
  • Giancarlo Sperlì, Flora Amato, Fabio Mercorio, Mario Mezzanzanica, Vincenzo Moscato, and Antonio Picariello, “A Social Media Recommender System,” International Journal of Multimedia Data Engineering and Management, vol. 9, no. 1, pp. 36–50, 2018. View at Publisher · View at Google Scholar
  • Nader Abdelaziz, Ragaa T. Abd El-Hakim, Sherif M. El-Badawy, and Hafez A. Afify, “International Roughness Index prediction model for flexible pavements,” International Journal of Pavement Engineering, pp. 1–12, 2018. View at Publisher · View at Google Scholar
  • Wook-Yeon Hwang, “Variable selection for collaborative filtering with market basket data,” International Transactions in Operational Research, 2018. View at Publisher · View at Google Scholar
  • Navid Hooshangi, and Ali Alesheikh, “Developing an Agent-Based Simulation System for Post-Earthquake Operations in Uncertainty Conditions: A Proposed Method for Collaboration among Agents,” ISPRS International Journal of Geo-Information, vol. 7, no. 1, pp. 27, 2018. View at Publisher · View at Google Scholar
  • Ahmed Hussein, Pablo Marín-Plaza, Fernando García, and José María Armingol, “Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation,” Journal of Advanced Transportation, vol. 2018, pp. 1–11, 2018. View at Publisher · View at Google Scholar
  • Katsuhiro Honda, Seiki Ubukata, and Akira Notsu, “A comparative study on effects of some exclusive conditions in fuzzy co-clustering for collaborative filtering,” Journal of Ambient Intelligence and Humanized Computing, 2018. View at Publisher · View at Google Scholar
  • Adrian Satja Kurdija, Marin Silic, Klemo Vladimir, and Goran Delac, “Efficient Global Correlation Measures for a Collaborative Filtering Dataset,” Knowledge-Based Systems, 2018. View at Publisher · View at Google Scholar
  • F. Ortega, B. Zhu, J. Bobadilla, and A. Hernando, “CF4J: Collaborative Filtering for Java,” Knowledge-Based Systems, 2018. View at Publisher · View at Google Scholar
  • Fatemeh Alyari, and Nima Jafari Navimipour, “Recommender systems,” Kybernetes, 2018. View at Publisher · View at Google Scholar
  • Tien Dat Nguyen, Taeseung Kim, Hyoseung Han, Hyun Yeong Shin, Canh Toan Nguyen, Hoa Phung, and Hyouk Ryeol Choi, “Characterization and optimization of flexible dual mode sensor based on Carbon Micro Coils,” Materials Research Express, vol. 5, no. 1, pp. 015604, 2018. View at Publisher · View at Google Scholar
  • Zhihai Yang, Qindong Sun, Yaling Zhang, and Beibei Zhang, “Uncovering anomalous rating behaviors for rating systems,” Neurocomputing, 2018. View at Publisher · View at Google Scholar
  • Xuehan Ye, Yongcai Wang, Yuhe Guo, Wei Hu, and Deying Li, “Accurate and Efficient Indoor Location by Dynamic Warping in Sequence-Type Radio-map,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 2, no. 1, pp. 1–22, 2018. View at Publisher · View at Google Scholar
  • Yan Li, and Yan Guo, “Cultural Distance-Aware Service Recommendation Approach in Mobile Edge Computing,” Scientific Programming, vol. 2018, pp. 1–8, 2018. View at Publisher · View at Google Scholar
  • Abdusselam Altunkaynak, and Tewodros Assefa Nigussie, “Monthly water demand prediction using wavelet transform, first-order differencing and linear detrending techniques based on multilayer perceptron models,” Urban Water Journal, pp. 1–5, 2018. View at Publisher · View at Google Scholar
  • Meenakshi Munjal, and Niraj Pratap Singh, “Utility aware network selection in small cell,” Wireless Networks, 2018. View at Publisher · View at Google Scholar
  • Koen Verstrepen, Kanishka Bhaduriy, Boris Cule, and Bart Goethals, “Collaborative Filtering for Binary, Positiveonly Data,” ACM SIGKDD Explorations Newsletter, vol. 19, no. 1, pp. 1–21, 2017. View at Publisher · View at Google Scholar
  • Liang Hu, Longbing Cao, Jian Cao, Zhiping Gu, Guandong Xu, and Jie Wang, “Improving the Quality of Recommendations for Users and Items in the Tail of Distribution,” ACM Transactions on Information Systems, vol. 35, no. 3, pp. 1–37, 2017. View at Publisher · View at Google Scholar
  • Lei Shi, Wayne Xin Zhao, and Yi-Dong Shen, “Local Representative-Based Matrix Factorization for Cold-Start Recommendation,” ACM Transactions on Information Systems, vol. 36, no. 2, pp. 1–28, 2017. View at Publisher · View at Google Scholar
  • Shanshan Feng, Jian Cao, Jie Wang, and Shiyou Qian, “Recommendations Based on Comprehensively Exploiting the Latent Factors Hidden in Items’ Ratings and Content,” ACM Transactions on Knowledge Discovery from Data, vol. 11, no. 3, pp. 1–27, 2017. View at Publisher · View at Google Scholar
  • Sneha Chaudhari, Amos Azaria, and Tom Mitchell, “An entity graph based Recommender System,” AI Communications, pp. 1–9, 2017. View at Publisher · View at Google Scholar
  • Xiuze Zhou, Weibo Shu, Fan Lin, and Beizhan Wang, “Confidence-weighted bias model for online collaborative filtering,” Applied Soft Computing, 2017. View at Publisher · View at Google Scholar
  • Qiufeng Wang, Kaizhu Huang, Song Li, and Wei Yu, “Adaptive modeling for large-scale advertisers optimization,” Big Data Analytics, vol. 2, no. 1, 2017. View at Publisher · View at Google Scholar
  • Xiangxiang Zeng, Ningxiang Ding, Alfonso Rodríguez-Patón, and Quan Zou, “Probability-based collaborative filtering model for predicting gene–disease associations,” BMC Medical Genomics, vol. 10, no. S5, 2017. View at Publisher · View at Google Scholar
  • Saravanan Sundaresan, Robin Doss, Selwyn Piramuthu, and Wanlei Zhou, “A Secure Search Protocol for Low Cost Passive RFID Tags,” Computer Networks, 2017. View at Publisher · View at Google Scholar
  • Mehdi Keshavarz Ghorabaee, Maghsoud Amiri, Edmundas Kazimieras Zavadskas, and Jurgita Antucheviciene, “Supplier evaluation and selection in fuzzy environments: a review of MADM approaches,” Economic Research-Ekonomska Istraživanja, vol. 30, no. 1, pp. 1073–1118, 2017. View at Publisher · View at Google Scholar
  • Muhammad Ali, Dae-Hee Son, Sang-Hee Kang, and Soon-Ryul Nam, “An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy,” Energies, vol. 10, no. 12, pp. 1830, 2017. View at Publisher · View at Google Scholar
  • Stijn Geuens, Kristof Coussement, and Koen W. De Bock, “A framework for configuring collaborative filtering-based recommendations derived from purchase data,” European Journal of Operational Research, 2017. View at Publisher · View at Google Scholar
  • Hyunwoo Hwangbo, and Yangsok Kim, “An Empirical Study on the Effect of Data Sparsity and Data Overlap on Cross Domain Collaborative Filtering Performance,” Expert Systems with Applications, 2017. View at Publisher · View at Google Scholar
  • Anu Taneja, and Anuja Arora, “Cross Domain Recommendation Using Multidimensional Tensor Factorization,” Expert Systems with Applications, 2017. View at Publisher · View at Google Scholar
  • Ke Ji, Zhenxiang Chen, Runyuan Sun, Kun Ma, Zhongjie Yuan, and Guandong Xu, “GIST: A Generative Model with Individual and Subgroup-based Topics For Group Recommendation,” Expert Systems with Applications, 2017. View at Publisher · View at Google Scholar
  • Rogério Nascimento de Carvalho, Guilherme Bastos Machado, and Marcelo José Colaço, “Estimating gasoline performance in internal combustion engines with simulation metamodels,” Fuel, vol. 193, pp. 230–240, 2017. View at Publisher · View at Google Scholar
  • Ahlem Kala?, Corinne Amel Zayani, Ikram Amous, and Wafa Abdelghani, “Social collaborative service recommendation approach based on user?s trust and domain-specific expertise,” Future Generation Computer Systems, 2017. View at Publisher · View at Google Scholar
  • Lifang Ren, and Wenjian Wang, “An SVM-based collaborative filtering approach for Top-N web services recommendation,” Future Generation Computer Systems, 2017. View at Publisher · View at Google Scholar
  • Jianqiang Li, Ji-Jiang Yang, Yu Zhao, Bo Liu, Mengchu Zhou, Jing Bi, and Qing Wang, “Enforcing Differential Privacy for Shared Collaborative Filtering,” IEEE Access, vol. 5, pp. 35–49, 2017. View at Publisher · View at Google Scholar
  • O-Joun Lee, Hoang Long Nguyen, Jai E. Jung, Tai-Won Um, and Hyun-Woo Lee, “Towards Ontological Approach on Trust-Aware Ambient Services,” IEEE Access, vol. 5, pp. 1589–1599, 2017. View at Publisher · View at Google Scholar
  • Jun Wu, Yu He, Xiangnan Guo, Yujia Zhang, and Na Zhao, “Heterogeneous Manifold Ranking for Image Retrieval,” IEEE Access, vol. 5, pp. 16871–16884, 2017. View at Publisher · View at Google Scholar
  • Shingo Kato, and Ryoichi Shinkuma, “Priority Control in Communication Networks for Accuracy-Freshness Tradeoff in Real-Time Road-Traffic Information Delivery,” IEEE Access, vol. 5, pp. 25226–25235, 2017. View at Publisher · View at Google Scholar
  • Xin Guan, Chang-Tsun Li, and Yu Guan, “Matrix Factorization With Rating Completion: An Enhanced SVD Model for Collaborative Filtering Recommender Systems,” IEEE Access, vol. 5, pp. 27668–27678, 2017. View at Publisher · View at Google Scholar
  • Lihong Peng, Bo Liao, Wen Zhu, Zejun Li, and Keqin Li, “Predicting Drug–Target Interactions With Multi-Information Fusion,” IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 2, pp. 561–572, 2017. View at Publisher · View at Google Scholar
  • Fran Casino, Constantinos Patsakis, Edgar Batista, Frederic Borras, and Antoni Martinez-Balleste, “Healthy Routes in the Smart City: A Context-Aware Mobile Recommender,” IEEE Software, vol. 34, no. 6, pp. 42–47, 2017. View at Publisher · View at Google Scholar
  • Xiwang Yang, Chao Liang, Miao Zhao, Hongwei Wang, Hao Ding, Yong Liu, Yang Li, and Junlin Zhang, “Collaborative Filtering-Based Recommendation of Online Social Voting,” IEEE Transactions on Computational Social Systems, vol. 4, no. 1, pp. 1–13, 2017. View at Publisher · View at Google Scholar
  • Yung-Yin Lo, Wanjiun Liao, Cheng-Shang Chang, and Ying-Chin Lee, “Temporal Matrix Factorization for Tracking Concept Drift in Individual User Preferences,” IEEE Transactions on Computational Social Systems, pp. 1–13, 2017. View at Publisher · View at Google Scholar
  • Siyuan Liu, and Shuhui Wang, “Trajectory Community Discovery and Recommendation by Multi-Source Diffusion Modeling,” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 4, pp. 898–911, 2017. View at Publisher · View at Google Scholar
  • Surong Yan, Kwei-Jay Lin, Xiaolin Zheng, Wenyu Zhang, and Xiaoqing Feng, “An Approach for Building Efficient and Accurate Social Recommender Systems Using Individual Relationship Networks,” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 10, pp. 2086–2099, 2017. View at Publisher · View at Google Scholar
  • Da Zheng, Disa Mhembere, Vince Lyzinski, Joshua T. Vogelstein, Carey E. Priebe, and Randal Burns, “Semi-External Memory Sparse Matrix Multiplication for Billion-Node Graphs,” IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 5, pp. 1470–1483, 2017. View at Publisher · View at Google Scholar
  • Rui Han, Siguang Huang, Zhentao Wang, and jianfeng Zhan, “CLAP: Component-level Approximate Processing for Low Tail Latency and High Result Accuracy in Cloud Online Services,” IEEE Transactions on Parallel and Distributed Systems, pp. 1–1, 2017. View at Publisher · View at Google Scholar
  • Jieming Zhu, Pinjia He, Zibin Zheng, and Michael R. Lyu, “Online QoS Prediction for Runtime Service Adaptation via Adaptive Matrix Factorization,” IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 10, pp. 2911–2924, 2017. View at Publisher · View at Google Scholar
  • Zhiwei Yu, Raymond K. Wong, and Chi-Hung Chi, “Efficient Role Mining for Context-Aware Service Recommendation Using a High-Performance Cluster,” IEEE Transactions on Services Computing, vol. 10, no. 6, pp. 914–926, 2017. View at Publisher · View at Google Scholar
  • Younghoon Kim, Wooyeol Kim, and Kyuseok Shim, “Latent ranking analysis using pairwise comparisons in crowdsourcing platforms,” Information Systems, vol. 65, pp. 7–21, 2017. View at Publisher · View at Google Scholar
  • Serkan Ballı, and Mustafa Tuker, “A Fuzzy Multi-Criteria Decision Analysis Approach for the Evaluation of the Network Service Providers in Turkey,” Intelligent Automation & Soft Computing, pp. 1–7, 2017. View at Publisher · View at Google Scholar
  • Yitao Wu, Xingming Zhang, Hong Yu, Shuai Wei, and Wei Guo, “Collaborative filtering recommendation algorithm based on user fuzzy similarity,” Intelligent Data Analysis, vol. 21, no. 2, pp. 311–327, 2017. View at Publisher · View at Google Scholar
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  • Ge Cui, Jun Luo, and Xin Wang, “Personalized travel route recommendation using collaborative filtering based on GPS trajectories,” International Journal of Digital Earth, pp. 1–24, 2017. View at Publisher · View at Google Scholar
  • Navid Hooshangi, and Ali Asghar Alesheikh, “Agent-Based Task Allocation under Uncertainties in Disaster Environments: an Approach to Interval Uncertainty,” International Journal of Disaster Risk Reduction, 2017. View at Publisher · View at Google Scholar
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  • Zhiyong Lin, Zhimin Xu, Dan Hu, Qinwu Hu, and Wenjing Li, “Hybrid Spatial Data Model for Indoor Space: Combined Topology and Grid,” ISPRS International Journal of Geo-Information, vol. 6, no. 11, pp. 343, 2017. View at Publisher · View at Google Scholar
  • Bei-Bei Cui, “Design and Implementation of Movie Recommendation System Based on Knn Collaborative Filtering Algorithm,” ITM Web of Conferences, vol. 12, pp. 04008, 2017. View at Publisher · View at Google Scholar
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  • Shantanu Pal, “Evaluating the impact of network loads and message size on mobile opportunistic networks in challenged environments,” Journal of Network and Computer Applications, vol. 81, pp. 47–58, 2017. View at Publisher · View at Google Scholar
  • Abdusselam Altunkaynak, and Tewodros Assefa Nigussie, “Monthly Water Consumption Prediction Using Season Algorithm and Wavelet Transform–Based Models,” Journal of Water Resources Planning and Management, pp. 04017011, 2017. View at Publisher · View at Google Scholar
  • Victor C. Cheng, Li Chen, William K. Cheung, and Chi-kuen Fok, “A heterogeneous hidden Markov model for mobile app recommendation,” Knowledge and Information Systems, 2017. View at Publisher · View at Google Scholar
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  • Shuo Yang, Mohammed Korayem, Khalifeh AlJadda, Trey Grainger, and Sriraam Natarajan, “Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach,” Knowledge-Based Systems, vol. 136, pp. 37–45, 2017. View at Publisher · View at Google Scholar
  • Ke Xu, Xushen Zheng, Yi Cai, Huaqing Min, Zhen Gao, Benjin Zhu, Haoran Xie, and Tak-Lam Wong, “Improving User Recommendation by Extracting Social Topics and Interest Topics of Users in Uni-Directional Social Networks,” Knowledge-Based Systems, 2017. View at Publisher · View at Google Scholar
  • Jianling Sun, Chenghao Liu, Tao Jin, Steven C. H. Hoi, and Peilin Zhao, “Collaborative topic regression for online recommender systems: an online and Bayesian approach,” Machine Learning, 2017. View at Publisher · View at Google Scholar
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  • Ruixu Zhou, Wensheng Gao, Bowen Zhang, Qinzhu Chen, Yafeng Liang, Dong Yao, Laijun Han, Xinzheng Liao, and Ruihai Li, “A new prediction model of daily weather elements in Hainan province under the typhoon weather,” Meteorology and Atmospheric Physics, 2017. View at Publisher · View at Google Scholar
  • Rahul Katarya, and Om Prakash Verma, “Efficient music recommender system using context graph and particle swarm,” Multimedia Tools and Applications, 2017. View at Publisher · View at Google Scholar
  • Han-Gyu Ko, In-Young Ko, and Dongman Lee, “Multi-criteria matrix localization and integration for personalized collaborative filtering in IoT environments,” Multimedia Tools and Applications, 2017. View at Publisher · View at Google Scholar
  • Wei Lu, Fu-lai Chung, Kunfeng Lai, and Liang Zhang, “Recommender system based on scarce information mining,” Neural Networks, 2017. View at Publisher · View at Google Scholar
  • Jicong Fan, and Jieyu Cheng, “Matrix completion by deep matrix factorization,” Neural Networks, 2017. View at Publisher · View at Google Scholar
  • Wenjuan Cui, Pengfei Wang, Yi Du, Xin Chen, Danhuai Guo, Jianhui Li, and Yuanchun Zhou, “An Algorithm for Event Detection Based on Social Media Data,” Neurocomputing, 2017. View at Publisher · View at Google Scholar
  • Xingyi Ren, Meina Song, Haihong E, and Junde Song, “Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation,” Neurocomputing, 2017. View at Publisher · View at Google Scholar
  • Jicong Fan, and Tommy Chow, “Deep Learning based Matrix Completion,” Neurocomputing, 2017. View at Publisher · View at Google Scholar