About this Journal Submit a Manuscript Table of Contents

Citations to this Journal [361 citations: 1–100 of 360 articles]

Articles published in Advances in Artificial Intelligence have been cited 361 times. The following is a list of the 360 articles that have cited the articles published in Advances in Artificial Intelligence.

  • Jennifer Moody, and David H. Glass, “A Novel Classification Framework for Evaluating Individual and Aggregate Diversity in Top-N Recommendations,” Acm Transactions On Intelligent Systems And Technology, vol. 7, no. 3, 2016. View at Publisher · View at Google Scholar
  • Le Wu, Qi Liu, Enhong Chen, Nicholas Jing Yuan, Guangming Guo, and Xing Xie, “Relevance Meets Coverage,” ACM Transactions on Intelligent Systems and Technology, vol. 7, no. 3, pp. 1–30, 2016. View at Publisher · View at Google Scholar
  • Suwoong Lee, Soono Kwon, Youngwoo Kim, and Kangwon Lee, “A method of vertical and horizontal force estimation by using air-filled material and camera for soft physical human-robot interaction: fundamental experiments,” Advanced Robotics, pp. 1–8, 2016. View at Publisher · View at Google Scholar
  • E. Earl Eiland, and Lorie M. Liebrock, “Efficacious Discriminant Analysis (Classifier) Measures for End Users,” Advances in Artificial Intelligence, vol. 2016, pp. 1–17, 2016. View at Publisher · View at Google Scholar
  • 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
  • M. Gopila, and I. Gnanambal, “An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique,” Circuits and Systems, vol. 07, no. 08, pp. 1569–1580, 2016. View at Publisher · View at Google Scholar
  • Mehdi Elahi, Francesco Ricci, and Neil Rubens, “A survey of active learning in collaborative filtering recommender systems,” Computer Science Review, 2016. View at Publisher · View at Google Scholar
  • Janghyeok Yoon, Wonchul Seo, Byoung-Youl Coh, Inseok Song, and Jae-Min Lee, “Identifying product opportunities using collaborative filtering-based patent analysis,” Computers & Industrial Engineering, 2016. View at Publisher · View at Google Scholar
  • Minsung Hong, and Jason J. Jung, “MyMovieHistory: Social Recommender System by Discovering Social Affinities Among Users,” Cybernetics and Systems, vol. 47, no. 1-2, pp. 88–110, 2016. View at Publisher · View at Google Scholar
  • Michiel Stock, Krzysztof Dembczyński, Bernard De Baets, and Willem Waegeman, “Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models,” Data Mining and Knowledge Discovery, 2016. View at Publisher · View at Google Scholar
  • Maria Rodriguez Fernandez, Adolfo Cortes Garcia, Ignacio Gonzalez Alonso, and Eduardo Zalama Casanova, “Using the Big Data generated by the Smart Home to improve energy efficiency management,” Energy Efficiency, vol. 9, no. 1, pp. 249–260, 2016. View at Publisher · View at Google Scholar
  • Li-Chen Cheng, Yen-Liang Chen, and Yu-Chia Chiang, “Identifying conflict patterns to reach a consensus – A novel group decision approach,” European Journal of Operational Research, 2016. View at Publisher · View at Google Scholar
  • Thomas Hart, and Lei Xie, “Providing data science support for systems pharmacology and its implications to drug discovery,” Expert Opinion on Drug Discovery, pp. 1–16, 2016. View at Publisher · View at Google Scholar
  • Edjalma Queiroz da Silva, Celso G. Camilo-Junior, Luiz Mario L. Pascoal, and Thierson C. Rosa, “An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering,” Expert Systems with Applications, 2016. View at Publisher · View at Google Scholar
  • 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
  • Zhe Yang, Bing Wu, Kan Zheng, Xianbin Wang, and Lei Lei, “A Survey of Collaborative Filtering-Based Recommender Systems for Mobile Internet Applications,” IEEE Access, vol. 4, pp. 3273–3287, 2016. View at Publisher · View at Google Scholar
  • Jevin D. West, Ian Wesley-Smith, and Carl T. Bergstrom, “A Recommendation System Based on Hierarchical Clustering of an Article-Level Citation Network,” IEEE Transactions on Big Data, vol. 2, no. 2, pp. 113–123, 2016. View at Publisher · View at Google Scholar
  • Giuseppe Araniti, Igor Bisio, Mauro De Sanctis, Antonino Orsino, and John Cosmas, “Multimedia Content Delivery for Emerging 5G-Satellite Networks,” IEEE Transactions on Broadcasting, vol. 62, no. 1, pp. 10–23, 2016. View at Publisher · View at Google Scholar
  • Jing Liu, Yu Jiang, Zechao Li, Xi Zhang, and Hanqing Lu, “Domain-Sensitive Recommendation with User-Item Subgroup Analysis,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 4, pp. 939–950, 2016. View at Publisher · View at Google Scholar
  • Cheng Chen, Lan Zheng, Venkatesh Srinivasan, Alex Thomo, Kui Wu, and Anthony Sukow, “Conflict-Aware Weighted Bipartite B-Matching and Its Application to E-Commerce,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 6, pp. 1475–1488, 2016. View at Publisher · View at Google Scholar
  • Manjusha Bhave, S. Janardhanan, and L. Dewan, “Configuration Control of Planar Underactuated Robotic Manipulator using Terminal Sliding Mode,” IFAC-PapersOnLine, vol. 49, no. 1, pp. 148–153, 2016. View at Publisher · View at Google Scholar
  • Won-Seok Hwang, Ho-Jong Lee, Sang-Wook Kim, Youngjoon Won, and Min-Soo Lee, “Efficient recommendation methods using category experts for a large dataset,” Information Fusion, vol. 28, pp. 75–82, 2016. View at Publisher · View at Google Scholar
  • Ondrej Kassak, Michal Kompan, and Maria Bielikova, “Personalized hybrid recommendation for group of users: Top-N multimedia recommender,” Information Processing & Management, vol. 52, no. 3, pp. 459–477, 2016. View at Publisher · View at Google Scholar
  • Chien Chin Chen, and Yu-Chun Sun, “Exploring acquaintances of social network site users for effective social event recommendations,” Information Processing Letters, vol. 116, no. 3, pp. 227–236, 2016. View at Publisher · View at Google Scholar
  • Jongwuk Lee, Dongwon Lee, Yeon-Chang Lee, Won-Seok Hwang, and Sang-Wook Kim, “Improving the Accuracy of Top-N Recommendation using a Preference Model,” Information Sciences, 2016. View at Publisher · View at Google Scholar
  • Fernando Ortega, Antonio Hernando, Jesus Bobadilla, and Jeon-Hyung Kang, “Recommending Items to Group of Users using Matrix Factorization based Collaborative Filtering,” Information Sciences, 2016. View at Publisher · View at Google Scholar
  • Rahul Kumar, and Pradip Kumar Bala, “Recommendation engine based on derived wisdom for more similar item neighbors,” Information Systems and e-Business Management, 2016. View at Publisher · View at Google Scholar
  • M. Radhika Mani, D. M. Potukuchi, and Ch. Satyanarayana, “A novel approach for shape-based object recognition with curvelet transform,” International Journal of Multimedia Information Retrieval, 2016. View at Publisher · View at Google Scholar
  • Yueshen Xu, Jianwei Yin, and Ying Li, “A collaborative framework of web service recommendation with clustering-extended matrix factorisation,” International Journal Of Web And Grid Services, vol. 12, no. 1, pp. 1–25, 2016. View at Publisher · View at Google Scholar
  • Dora Melo, Irene Pimenta Rodrigues, and Vitor Beires Nogueira, “Using a Dialogue Manager to Improve Semantic Web Search,” International Journal on Semantic Web and Information Systems, vol. 12, no. 1, pp. 62–78, 2016. View at Publisher · View at Google Scholar
  • Ibrahim Mashal, Osama Alsaryrah, and Tein-Yaw Chung, “Testing and evaluating recommendation algorithms in internet of things,” Journal of Ambient Intelligence and Humanized Computing, 2016. View at Publisher · View at Google Scholar
  • Abdusselam Altunkaynak, and Mehmet Ozger, “Comparison of Discrete and Continuous Wavelet–Multilayer Perceptron Methods for Daily Precipitation Prediction,” Journal of Hydrologic Engineering, pp. 04016014, 2016. View at Publisher · View at Google Scholar
  • Yi Huang, “Personalized Recommendation of Coupon Deals by Keywords Association Rules,” Journal of Industrial and Intelligent Information, 2016. View at Publisher · View at Google Scholar
  • Cristina Renzi, and Francesco Leali, “A Multicriteria Decision-Making Application to the Conceptual Design of Mechanical Components,” Journal of Multi-Criteria Decision Analysis, 2016. View at Publisher · View at Google Scholar
  • Peio Lopez-Iturri, Fran Casino, Erik Aguirre, Leyre Azpilicueta, Francisco Falcone, and Agusti Solanas, “Performance Analysis of ZigBee Wireless Networks for AAL through Hybrid Ray Launching and Collaborative Filtering,” Journal of Sensors, vol. 2016, pp. 1–16, 2016. View at Publisher · View at Google Scholar
  • Haijun Zhang, Bo Zhang, Zhenping Li, and Guicheng Shen, “Exploit Rating Scale Model for Collaborative Filtering,” Journal of Software, vol. 11, no. 6, pp. 528–537, 2016. View at Publisher · View at Google Scholar
  • Yoshinori Nakanishi-Ohno, Tomoyuki Obuchi, Masato Okada, and Yoshiyuki Kabashima, “Sparse approximation based on a random overcomplete basis,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2016, no. 6, pp. 063302, 2016. View at Publisher · View at Google Scholar
  • Mahdi Nasiri, and Behrouz Minaei, “Increasing prediction accuracy in collaborative filtering with initialized factor matrices,” Journal Of Supercomputing, vol. 72, no. 6, pp. 2157–2169, 2016. View at Publisher · View at Google Scholar
  • F. Petroni, L. Querzoni, R. Beraldi, and M. Paolucci, “LCBM: a fast and lightweight collaborative filtering algorithm for binary ratings,” Journal Of Systems And Software, vol. 117, pp. 583–594, 2016. View at Publisher · View at Google Scholar
  • Ido Blank, Lior Rokach, and Guy Shani, “Leveraging metadata to recommend keywords for academic papers,” Journal of the Association for Information Science and Technology, 2016. View at Publisher · View at Google Scholar
  • Feng Zhang, Ti Gong, Victor E. Lee, Gansen Zhao, Chunming Rong, and Guangzhi Qu, “Fast Algorithms to Evaluate Collaborative Filtering Recommender Systems,” Knowledge-Based Systems, 2016. View at Publisher · View at Google Scholar
  • Hao Wu, Kun Yue, Yijian Pei, Bo Li, Yiji Zhao, and Fan Dong, “Collaborative Topic Regression with Social Trust Ensemble for Recommendation in Social Media Systems,” Knowledge-Based Systems, 2016. View at Publisher · View at Google Scholar
  • Shanfeng Wang, Maoguo Gong, Haoliang Li, and Junwei Yang, “Multi-objective optimization for long tail recommendation,” Knowledge-Based Systems, 2016. View at Publisher · View at Google Scholar
  • Rouzbeh Meymandpour, and Joseph G. Davis, “A Semantic Similarity Measure for Linked Data: An Information Content-Based Approach,” Knowledge-Based Systems, 2016. View at Publisher · View at Google Scholar
  • Omar Besbes, Yonatan Gur, and Assaf Zeevi, “Optimization in Online Content Recommendation Services: Beyond Click-Through Rates,” M&Som-Manufacturing & Service Operations Management, vol. 18, no. 1, pp. 15–33, 2016. View at Publisher · View at Google Scholar
  • Shasha Lu, Li Xiao, and Min Ding, “A Video-Based Automated Recommender (VAR) System for Garments,” Marketing Science, 2016. View at Publisher · View at Google Scholar
  • Zhi Yang Jia, Wei Gao, and Yi Jin Shi, “An Agent Framework of Tourism Recommender System,” MATEC Web of Conferences, vol. 44, pp. 01005, 2016. View at Publisher · View at Google Scholar
  • Haijun Zhang, Bo Zhang, Zhoujun Li, Guicheng Shen, and Liping Tian, “Transfer Learning for Collaborative Filtering Using a Psychometrics Model,” Mathematical Problems in Engineering, vol. 2016, pp. 1–9, 2016. View at Publisher · View at Google Scholar
  • Ilaria Bartolini, Vincenzo Moscato, Ruggero G. Pensa, Antonio Penta, Antonio Picariello, Carlo Sansone, and Maria Luisa Sapino, “Recommending multimedia visiting paths in cultural heritage applications,” Multimedia Tools And Applications, vol. 75, no. 7, pp. 3813–3842, 2016. View at Publisher · View at Google Scholar
  • Jose F Rodrigues, Fernando V Paulovich, Maria CF de Oliveira, and Osvaldo N de Oliveira, “On the convergence of nanotechnology and Big Data analysis for computer-aided diagnosis,” Nanomedicine, 2016. View at Publisher · View at Google Scholar
  • Cong Zheng, Haihong E, Meina Song, and Junde Song, “CMPTF: Contextual Modeling Probabilistic Tensor Factorization for Recommender Systems,” Neurocomputing, 2016. View at Publisher · View at Google Scholar
  • Ke Ji, and Hong Shen, “Jointly Modeling Content, Social Network and Ratings for Explainable and Cold-start Recommendation,” Neurocomputing, 2016. View at Publisher · View at Google Scholar
  • Furong Peng, Jianfeng Lu, Yongli Wang, Richard Yi-Da Xu, Chao Ma, and Jingyu Yang, “N-dimensional Markov Random Field Prior for Cold-start Recommendation,” Neurocomputing, 2016. View at Publisher · View at Google Scholar
  • Andrea Vassallo, Michela Chiappalone, Ricardo De Camargos Lopes, Bibiana Scelfo, Antonio Novellino, Enrico Defranchi, Taina Palosaari, Timo Weisschu, Tzutzuy Ramirez, Sergio Martinoia, Andrew F.M. Johnstone, Cina M. Mack, Robert Landsiedel, Maurice Whealan, Anna Bal-Price, and Timothy J. Shafer, “A multi-laboratory evaluation of microelectrode array-based measurements of neural network activity for acute neurotoxicity testing,” NeuroToxicology, 2016. View at Publisher · View at Google Scholar
  • Alejandro Baldominos, Javier Calle, and Dolores Cuadra, “Beyond social graphs: mining patterns underlying social interactions,” Pattern Analysis and Applications, 2016. View at Publisher · View at Google Scholar
  • Nirmal Jonnalagedda, Susan Gauch, Kevin Labille, and Sultan Alfarhood, “Incorporating popularity in a personalized news recommender system,” PeerJ Computer Science, vol. 2, pp. e63, 2016. View at Publisher · View at Google Scholar
  • Tinghuai Ma, Xiafei Suo, Jinjuan Zhou, Meili Tang, Donghai Guan, Yuan Tian, Abdullah Al-Dhelaan, and Mznah Al-Rodhaan, “Augmenting matrix factorization technique with the combination of tags and genres,” Physica A: Statistical Mechanics and its Applications, 2016. View at Publisher · View at Google Scholar
  • Ibrahim Mashal, Osama Alsaryrah, and Tein-Yaw Chung, “Performance evaluation of recommendation algorithms on Internet of Things services,” Physica A: Statistical Mechanics and its Applications, 2016. View at Publisher · View at Google Scholar
  • Fei Yu, An Zeng, Sébastien Gillard, and Matúš Medo, “Network-based recommendation algorithms: A review,” Physica A: Statistical Mechanics and its Applications, 2016. View at Publisher · View at Google Scholar
  • Yong Liu, Min Wu, Chunyan Miao, Peilin Zhao, and Xiao-Li Li, “Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction,” Plos Computational Biology, vol. 12, no. 2, 2016. View at Publisher · View at Google Scholar
  • Weijie Cheng, Guisheng Yin, Yuxin Dong, Hongbin Dong, and Wansong Zhang, “Collaborative Filtering Recommendation on Users' Interest Sequences,” Plos One, vol. 11, no. 5, 2016. View at Publisher · View at Google Scholar
  • Tito Tomo, Sophon Somlor, Alexander Schmitz, Lorenzo Jamone, Weijie Huang, Harris Kristanto, and Shigeki Sugano, “Design and Characterization of a Three-Axis Hall Effect-Based Soft Skin Sensor,” Sensors, vol. 16, no. 4, pp. 491, 2016. View at Publisher · View at Google Scholar
  • Chengyuan Yu, and Linpeng Huang, “CluCF: a clustering CF algorithm to address data sparsity problem,” Service Oriented Computing and Applications, 2016. View at Publisher · View at Google Scholar
  • Shuma Adhikari, Nidul Sinha, and Thingam Dorendrajit, “Fuzzy logic based on-line fault detection and classification in transmission line,” SpringerPlus, vol. 5, no. 1, 2016. View at Publisher · View at Google Scholar
  • Riaan Rudman, and Rikus Bruwer, “Defining Web 3.0: opportunities and challenges,” The Electronic Library, vol. 34, no. 1, pp. 132–154, 2016. View at Publisher · View at Google Scholar
  • Md Moniruzzaman, Hanna Maoh, and William Anderson, “Short-term prediction of border crossing time and traffic volume for commercial trucks: A case study for the Ambassador Bridge,” Transportation Research Part C: Emerging Technologies, vol. 63, pp. 182–194, 2016. View at Publisher · View at Google Scholar
  • Cheronie Shely Stanis, Beng Kah Song, Tock Hing Chua, Yee Ling Lau, and Jenarun Jelip, “Evaluation of new multiplex PCR primers for the identification of Plasmodium species found in Sabah, Malaysia,” Turkish Journal Of Medical Sciences, vol. 46, no. 1, pp. 207–218, 2016. View at Publisher · View at Google Scholar
  • Nedia Araibi, Eya Ben Ahmed, and Wahiba Karaa Ben Abdessalem, “$$\mathcal {IRORS}$$ IRORS : intelligent recommendation of RSS feeds,” Vietnam Journal of Computer Science, 2016. View at Publisher · View at Google Scholar
  • Suwoong Lee, Hidetaka Nozawa, Daisuke Watanabe, and Kenji Inoue, “Force estimation via physical properties of air cushion for the control of a human-cooperative robot: basic experiments,” Advanced Robotics, vol. 29, no. 2, pp. 139–146, 2015. View at Publisher · View at Google Scholar
  • Sophon Somlor, Richard Sahala Hartanto, Alexander Schmitz, and Shigeki Sugano, “A novel tri-axial capacitive-type skin sensor,” Advanced Robotics, vol. 29, no. 21, pp. 1375–1391, 2015. View at Publisher · View at Google Scholar
  • A. Pergament, G. Stefanovich, V. Malinenko, and A. Velichko, “Electrical Switching in Thin Film Structures Based on Transition Metal Oxides,” Advances in Condensed Matter Physics, vol. 2015, pp. 1–26, 2015. View at Publisher · View at Google Scholar
  • Mikael Collan, Mario Fedrizzi, and Pasi Luukka, “New Closeness Coefficients for Fuzzy Similarity Based Fuzzy TOPSIS: An Approach Combining Fuzzy Entropy and Multidistance,” Advances in Fuzzy Systems, vol. 2015, pp. 1–12, 2015. View at Publisher · View at Google Scholar
  • Krishna Chaitanya Patchava, Mohammed Benaissa, Bilal Malik, and Hatim Behairy, “Local linear embedded regression in the quantitative analysis of glucose in near infrared spectra,” Analytical Methods, vol. 7, no. 4, pp. 1484–1492, 2015. View at Publisher · View at Google Scholar
  • Eunhui Kim, and Munchurl Kim, “Topic-tracking-based dynamic user modeling with TV recommendation applications,” Applied Intelligence, 2015. View at Publisher · View at Google Scholar
  • Gang Luo, Flory L. Nkoy, Bryan L. Stone, Darell Schmick, and Michael D. Johnson, “A systematic review of predictive models for asthma development in children,” BMC Medical Informatics and Decision Making, vol. 15, no. 1, 2015. View at Publisher · View at Google Scholar
  • Boudjelal Meftah, Olivier Lézoray, and Abdelkader Benyettou, “Novel Approach Using Echo State Networks for Microscopic Cellular Image Segmentation,” Cognitive Computation, 2015. View at Publisher · View at Google Scholar
  • Faezeh S. Gohari, and Mohammad Jafar Tarokh, “New Recommender Framework: Combining Semantic Similarity Fusion and Bicluster Collaborative Filtering,” Computational Intelligence, 2015. View at Publisher · View at Google Scholar
  • Duy Vu, and Murray Aitkin, “Variational algorithms for biclustering models,” Computational Statistics & Data Analysis, 2015. View at Publisher · View at Google Scholar
  • Robert Herzog, Daniel Mewes, Michael Wand, Leonidas Guibas, and Hans-Peter Seidel, “LeSSS: Learned Shared Semantic Spaces for Relating Multi-Modal Representations of 3D Shapes,” Computer Graphics Forum, vol. 34, no. 5, pp. 141–151, 2015. View at Publisher · View at Google Scholar
  • Seyed Mohammadhadi Daneshmand, Amin Javari, Seyed Ebrahim Abtahi, and Mahdi Jalili, “A Time-Aware Recommender System Based on Dependency Network of Items,” Computer Journal, vol. 58, no. 9, pp. 1955–1966, 2015. View at Publisher · View at Google Scholar
  • Rahman Ali, Muhammad Afzal, Maqbool Hussain, Maqbool Ali, Muhammad Hameed Siddiqi, Sungyoung Lee, and Byeong Ho Kang, “Multimodal Hybrid Reasoning Methodology for Personalized Wellbeing Services,” Computers in Biology and Medicine, 2015. View at Publisher · View at Google Scholar
  • Edward Rolando Núñez-Valdez, Juan Manuel Cueva Lovelle, Guillermo Infante Hernández, Aquilino Juan Fuente, and José Emilio Labra-Gayo, “Creating recommendations on electronic books: A collaborative learning implicit approach,” Computers in Human Behavior, 2015. View at Publisher · View at Google Scholar
  • Vincenza Carchiolo, Alessandro Longheu, Michele Malgeri, and Giuseppe Mangioni, “Searching for experts in a context-aware recommendation network,” Computers in Human Behavior, 2015. View at Publisher · View at Google Scholar
  • Francesco Colace, Massimo De Santo, Luca Greco, Vincenzo Moscato, and Antonio Picariello, “A collaborative user-centered framework for recommending items in Online Social Networks,” Computers in Human Behavior, 2015. View at Publisher · View at Google Scholar
  • Antoine Boutet, Davide Frey, Rachid Guerraoui, Arnaud Jégou, and Anne-Marie Kermarrec, “Privacy-preserving distributed collaborative filtering,” Computing, 2015. View at Publisher · View at Google Scholar
  • Mingdong Tang, Tingting Zhang, Jianxun Liu, and Jinjun Chen, “Cloud service QoS prediction via exploiting collaborative filtering and location-based data smoothing,” Concurrency And Computation-Practice & Experience, vol. 27, no. 18, pp. 5826–5839, 2015. View at Publisher · View at Google Scholar
  • Huiji Gao, Jiliang Tang, and Huan Liu, “Addressing the cold-start problem in location recommendation using geo-social correlations,” Data Mining And Knowledge Discovery, vol. 29, no. 2, pp. 299–323, 2015. View at Publisher · View at Google Scholar
  • Cuiqing Jiang, Rui Duan, Hemant K. Jain, Shixi Liu, and Kun Liang, “Hybrid Collaborative Filtering For High-Involvement Products: A Solution to Opinion Sparsity and Dynamics,” Decision Support Systems, 2015. View at Publisher · View at Google Scholar
  • Malak Al-Hassan, Haiyan Lu, and Jie Lu, “A Semantic Enhanced Hybrid Recommendation Approach: a Case Study of E-government Tourism Service Recommendation System,” Decision Support Systems, 2015. View at Publisher · View at Google Scholar
  • Ying-Si Zhao, Yan-Ping Liu, and Qing-An Zeng, “A weight-based item recommendation approach for electronic commerce systems,” Electronic Commerce Research, 2015. View at Publisher · View at Google Scholar
  • Yueshen Xu, and Jianwei Yin, “Collaborative recommendation with user generated content,” Engineering Applications of Artificial Intelligence, vol. 45, pp. 281–294, 2015. View at Publisher · View at Google Scholar
  • Parham Moradi, and Sajad Ahmadian, “A reliability-based recommendation method to improve trust-aware recommender systems,” Expert Systems with Applications, 2015. View at Publisher · View at Google Scholar
  • Nikolaos Polatidis, and Christos K. Georgiadis, “A multi-level collaborative filtering method that improves recommendations,” Expert Systems with Applications, 2015. View at Publisher · View at Google Scholar
  • Santiago Cifuentes, Jose María Girón-Sierra, and Juan Jiménez, “Virtual fields and behaviour blending for the coordinated navigation of robot teams: Some experimental results.,” Expert Systems with Applications, 2015. View at Publisher · View at Google Scholar
  • Anupriya Gogna, and Angshul Majumdar, “Matrix Completion Incorporating Auxiliary Information for Recommender System Design,” Expert Systems with Applications, 2015. View at Publisher · View at Google Scholar
  • Dimah H. Alahmadi, and Xaio-Jun Zeng, “ISTS: Implicit Social Trust and Sentiment based Approach to Recommender Systems,” Expert Systems with Applications, 2015. View at Publisher · View at Google Scholar
  • Anupriya Gogna, and Angshul Majumdar, “A Comprehensive Recommender System Model: Improving Accuracy for Both Warm and Cold Start Users,” Ieee Access, vol. 3, pp. 2803–2813, 2015. View at Publisher · View at Google Scholar
  • David Silvera-Tawil, David Rye, Manuchehr Soleimani, and Mari Velonaki, “Electrical Impedance Tomography for Artificial Sensitive Robotic Skin: A Review,” Ieee Sensors Journal, vol. 15, no. 4, pp. 2001–2016, 2015. View at Publisher · View at Google Scholar
  • Alexander Zook, and Mark O. Riedl, “Temporal Game Challenge Tailoring,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 7, no. 4, pp. 336–346, 2015. View at Publisher · View at Google Scholar
  • Saravanan Sundaresan, Robin Doss, and Wanlei Zhou, “Zero Knowledge Grouping Proof Protocol for RFID EPC C1G2 Tags,” IEEE Transactions on Computers, vol. 64, no. 10, pp. 2994–3008, 2015. View at Publisher · View at Google Scholar