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WORLDCOMP'14 Tutorial: Prof. Samee U. Khan and Mr. Muhammad Usman Shahid Khan

Last modified 2014-07-04 17:40

Recommendation Systems for big data


Prof. Samee U. Khan & Mr. Muhammad Usman Shahid Khan
North Dakota State University, USA

Date: July 23, 2014 - 5:45pm
Location: Ballroom 3


Abstract

    Recommender systems are special information filtering systems that predict the users’ preference in the near future. Recommendation systems have been developed since the 90s and apply numerous knowledge discovery techniques on users’ historical and contextual data to suggest information, products, and services that best match the user’s preferences. With the advancement in communication infrastructure and easy access of e-commerce and mobile social network applications, such as Amazon, Facebook, Twitter, and Foursquare have resulted in tremendous growth in the volume of the data. According to International Data Corporation the data consumed only in the USA is expected to be doubled in every three years from 2012 to 2020. Moreover, the MIT Slogan Review has presented the result of the survey that the top firms are investing five to six percent more on their data to extract valuable information than the lower performers. The competition in the industry and the huge volume of the data demand newer and better solutions for the recommender systems to address the challenges that are being caused by the big data. In this tutorial, we will consider the future research directions in the recommender systems and the challenges caused by the big data on recommender system, foremost among them are

    • Scalability
    • Data sparseness
    • Cold start
    • Long Tail

Objectives

    This tutorial will enable you to understand

    • What are recommender systems and how they predict personalized and group recommendations?
    • Why new recommender systems are mandatory to respond big data demands?
    • What are the open challenges for recommender systems in a big data era?
    • What are the future research directions in recommender systems?

Biography of Instructors

    Prof. Samee U. Khan received a BS degree from Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan, and a PhD from the University of Texas, Arlington, TX, USA. Currently, he is Assistant Professor of Electrical and Computer Engineering at the North Dakota State University, Fargo, ND, USA. Prof. Khan’s research interests include optimization, robustness, and security of: cloud, grid, cluster and big data computing, social networks, wired and wireless networks, power systems, smart grids, and optical networks. His work has appeared in over 225 publications. He is a Fellow of the Institution of Engineering and Technology (IET, formerly IEE), and a Fellow of the British Computer Society (BCS).

    Mr. Muhammad Usman Shahid Khan received his bachelors of computer science degree from Bahauddin Zakariya University, Pakistan and a master degree in Information Security from National University of Science and Technology, Pakistan. He served as a lecturer at COMSATS Institute of Information Technology from 2008 to 2012. Currently, he is pursuing his PhD in Electrical and Computer Engineering at North Dakota State University, Fargo, ND, USA. His areas of interest are recommendation systems, data mining, big data and network security.

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