Dr. Maizatul Akmar Ismail, Associate ProfessorDepartment of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
Speech Title: Recommendation Systems Approaches in Big Data
Abstract: Intelligent data handling techniques are beneficial for users; to store, process, analyze and access the vast amount of information produced by electronic and automated devices. The leading approach is to use recommender systems (RS) to extract relevant information from the vast amount of knowledge, and quickly aid the process of information seeking. This talk will focus on the applications of RS in E-Commerce, E-Learning, Multimedia and Tourism domains. The future research direction of RS in Big Data will also be discussed, particularly on Context-Aware Recommender Systems (CARS) as a leading solution to big data challenges.
Biography: Dr. Maizatul Akmar Ismail is an Associate Professor at the Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya (UM), Malaysia. Her academic qualifications were obtained from Universiti Malaya (UM) for her Bachelor's and Ph.D. degree, and the University of Putra Malaysia for her master's. At present, she has more than twenty years of teaching experience since she started her career as a lecturer at the Universiti Malaya. Maizatul was involved in various research, leading to the publication of several academic papers in the areas of Information Systems specifically on Educational Technology, Recommender Systems, and Data Mining. She has been actively publishing more than 70 conference papers at renowned local and international conferences. A number of her works were also published in reputable international journals. Maizatul has participated in many competitions and exhibitions to promote her research works. She has been appointed as Competition Judges for several innovation competitions. She hopes to extend her research beyond Information Systems in her quest to elevate the quality of teaching and learning.
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