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Citations to this Journal [473 citations: 1–100 of 472 articles]

Articles published in Advances in Artificial Intelligence have been cited 473 times. The following is a list of the 472 articles that have cited the articles published in Advances in Artificial Intelligence.

  • 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
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  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • B. N. Sharma, J. Raj, and J. Vanualailai, “Navigation of carlike robots in an extended dynamic environment with swarm avoidance,” International Journal of Robust and Nonlinear Control, 2017. View at Publisher · View at Google Scholar
  • Songtao Shang, Wenqian Shang, Minyong Shi, Shuchao Feng, and Zhiguo Hong, “A Video Recommendation Algorithm Based on Hyperlink-Graph Model,” International Journal of Software Innovation, vol. 5, no. 3, pp. 49–63, 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
  • Anis Sharafoddini, Joel A Dubin, and Joon Lee, “Patient Similarity in Prediction Models Based on Health Data: A Scoping Review,” JMIR Medical Informatics, vol. 5, no. 1, pp. e7, 2017. View at Publisher · View at Google Scholar
  • Sanjay Singh, Chandra Shekhar, and Anil Vohra, “Real-Time FPGA-Based Object Tracker with Automatic Pan-Tilt Features for Smart Video Surveillance Systems,” Journal of Imaging, vol. 3, no. 2, pp. 18, 2017. View at Publisher · View at Google Scholar
  • Luis Omar Colombo-Mendoza, Rafael Valencia-García, Alejandro Rodríguez-González, Ricardo Colomo-Palacios, and Giner Alor-Hernández, “Towards a knowledge-based probabilistic and context-aware social recommender system,” Journal of Information Science, pp. 016555151769878, 2017. View at Publisher · View at Google Scholar
  • Alexandra Oliveira, Brígida Mónica Faria, A. Rita Gaio, and Luís Paulo Reis, “Data Mining in HIV-AIDS Surveillance System,” Journal of Medical Systems, vol. 41, no. 4, 2017. View at Publisher · View at Google Scholar
  • 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
  • Chaochao Chen, Kevin Chen-Chuan Chang, and Xiaolin Zheng, “Towards Context-Aware Social Recommendation via Individual Trust,” Knowledge-Based Systems, 2017. View at Publisher · View at Google Scholar
  • 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
  • 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
  • Andrew Jones, and Jeremy Straub, “Concepts for 3D Printing-Based Self-Replicating Robot Command and Coordination Techniques,” Machines, vol. 5, no. 2, pp. 12, 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
  • 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
  • Dong-Kyu Chae, Sang-Chul Lee, Si-Yong Lee, and Sang-Wook Kim, “On Identifying k -Nearest Neighbors in Neighborhood Models for Efficient and Effective Collaborative Filtering (invited paper),” Neurocomputing, 2017. View at Publisher · View at Google Scholar
  • Jicong Fan, and Tommy W.S. Chow, “Matrix completion by least-square, low-rank, and sparse self-representations,” Pattern Recognition, 2017. View at Publisher · View at Google Scholar
  • Jicong Fan, and Tommy W.S. Chow, “Non-Linear Matrix Completion,” Pattern Recognition, 2017. View at Publisher · View at Google Scholar
  • Wen-Jun Li, Qiang Dong, Yang-Bo Shi, Yan Fu, and Jia-Lin He, “Effect of recent popularity on heat-conduction based recommendation models,” Physica A: Statistical Mechanics and its Applications, vol. 474, pp. 334–343, 2017. View at Publisher · View at Google Scholar
  • Junliang Yu, Min Gao, Wenge Rong, Qingyu Xiong, and Junhao Wen, “Hybrid attacks on model-based social recommender systems,” Physica A: Statistical Mechanics and its Applications, 2017. View at Publisher · View at Google Scholar
  • Hisashi Fujii, and Tomoko Kojiri, “Advice-Sharing Environment for Acquiring Motor Skills,” Procedia Computer Science, vol. 112, pp. 1954–1963, 2017. View at Publisher · View at Google Scholar
  • Chandra Sekhar Mohanty, Partha Sarathi Khuntia, and Debjani Mitra, “Design of Stable Nonlinear Pitch Control System for a Jet Aircraft by Using Artificial Intelligence,” Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 2017. View at Publisher · View at Google Scholar
  • Sunil Kr. Jha, Jasmin Bilalovic, Anju Jha, Nilesh Patel, and Han Zhang, “Renewable energy: Present research and future scope of Artificial Intelligence,” Renewable and Sustainable Energy Reviews, vol. 77, pp. 297–317, 2017. View at Publisher · View at Google Scholar
  • Lingfeng Bao, David Lo, Xin Xia, and Shanping Li, “Automated Android application permission recommendation,” Science China Information Sciences, vol. 60, no. 9, 2017. View at Publisher · View at Google Scholar
  • Xiaoying Tan, Yuchun Guo, Yishuai Chen, and Wei Zhu, “Accurate inference of user popularity preference in a large-scale online video streaming system,” Science China Information Sciences, vol. 61, no. 1, 2017. View at Publisher · View at Google Scholar
  • Hongtao Wang, Hui Wen, Feng Yi, Hongsong Zhu, and Limin Sun, “Road Traffic Anomaly Detection via Collaborative Path Inference from GPS Snippets,” Sensors, vol. 17, no. 3, pp. 550, 2017. View at Publisher · View at Google Scholar
  • Paris V. Giampouras, Athanasios A. Rontogiannis, Konstantinos E. Themelis, and Konstantinos D. Koutroumbas, “Online Sparse and Low-Rank Subspace Learning from Incomplete Data: A Bayesian View,” Signal Processing, 2017. View at Publisher · View at Google Scholar
  • Weiyi Qian, and Ming Li, “Convergence analysis of standard particle swarm optimization algorithm and its improvement,” Soft Computing, 2017. View at Publisher · View at Google Scholar
  • Dae-Young Kim, Young-Sik Jeong, and Seokhoon Kim, “Data-Filtering System to Avoid Total Data Distortion in IoT Networking,” Symmetry, vol. 9, no. 1, pp. 16, 2017. View at Publisher · View at Google Scholar
  • Mingook Lee, and Sungjoo Lee, “Identifying new business opportunities from competitor intelligence: An integrated use of patent and trademark databases,” Technological Forecasting and Social Change, vol. 119, pp. 170–183, 2017. View at Publisher · View at Google Scholar
  • Ankhtuya Ochirbat, Timothy K. Shih, Chalothon Chootong, Worapot Sommool, W.K.T.M. Gunarathne, Wang Hai-Hui, and Ma Zhao-Heng, “Hybrid Occupation Recommendation for Adolescents on Interest, Profile, and Behavior,” Telematics and Informatics, 2017. View at Publisher · View at Google Scholar
  • Karamollah Bagheri Fard, Mohsen Rahmani, Mehrbakhsh Nilashi, and Vahid Rafe, “Performance Improvement for Recommender Systems Using Ontology,” Telematics and Informatics, 2017. View at Publisher · View at Google Scholar
  • Wei-Ta Chu, and Ya-Lun Tsai, “A hybrid recommendation system considering visual information for predicting favorite restaurants,” World Wide Web, 2017. View at Publisher · View at Google Scholar
  • Liang Hu, Longbing Cao, Jian Cao, Zhiping Gu, Guandong Xu, and Dingyu Yang, “Learning Informative Priors from Heterogeneous Domains to Improve Recommendation in Cold-Start User Domains,” ACM Transactions on Information Systems, vol. 35, no. 2, pp. 1–37, 2016. View at Publisher · View at Google Scholar
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  • Maria-Iuliana Dascalu, Constanta-Nicoleta Bodea, Monica Nastasia Mihailescu, Elena Alice Tanase, and Patricia Ordoñez de Pablos, “Educational recommender systems and their application in lifelong learning,” Behaviour & Information Technology, pp. 1–8, 2016. View at Publisher · View at Google Scholar
  • Fredrick M. Mobegi, Aldert Zomer, Marien I. de Jonge, and Sacha A. F. T. van Hijum, “Advances and perspectives in computational prediction of microbial gene essentiality,” Briefings in Functional Genomics, 2016. View at Publisher · View at Google Scholar
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  • Yueshen Xu, Jianwei Yin, Shuiguang Deng, Neal N. Xiong, and Jianbin Huang, “Context-aware QoS Prediction for Web Service Recommendation and Selection,” Expert Systems with Applications, 2016. View at Publisher · View at Google Scholar
  • Diego Sánchez-Moreno, Ana B. Gil González, M. Dolores Muñoz Vicente, Vivian F. López Batista, and María N. Moreno García, “A collaborative filtering method for music recommendation using playing coefficients for artists and users,” Expert Systems with Applications, vol. 66, pp. 234–244, 2016. View at Publisher · View at Google Scholar
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