| About the book | |
The proliferation of Internet technologies and e-commerce has made the webspace an exciting and interactive business platform for producers, marketers and consumers. At the same time, web itself has become complex and difficult to navigate, overwhelming users with innumerable choices of products, services, and/or information. But, help is at hand with recommender systems which can overcome the information overload problem by retrieving appropriate information based on a user's past purchases, tastes and preferences and those of similar users. Written by seasoned IT experts with extensive research and industry experience, Recommender Systems in e-Commerce deals with recommendation systems, process and techniques in detail. Enriched with illustrative diagrams and examples, the book covers: The book will be useful for professionals working in e-commerce industry, management students, faculty and researchers | |
| About the author | |
Bharat Bhasker Dr. Bharat Bhasker is currently the Professor of Information Technology and Systems, Head of Internet Commerce Research Center and former Dean of the Indian Institute of Management (IIM), Lucknow. Dr. Bhasker is also a Visiting Professor at the ESSEC Business School, France, Chung-Ang University, Seoul, Korea; School of Business, University of Maryland, US; University of California, US; and University of Texas, US. He holds a Bachelor's degree in Electronics & Communications Engineering from I.I.T. Roorkee; and Master's and Doctorate degrees in Computer Science from Virginia Polytechnic Institute and State University, USA. Prior to joining IIM-Lucknow, He spent 11 years working in the leading US research establishments including NASA's Goddard Space Flight Center. His research contributions in Heterogeneous Distributed Database Management Systems, Heterogeneous Distributed Information Management, Information Retrieval from Mass Repositories, and Network Information Retrieval Protocols fetched him NASA's Best Research Productivity Award (1992). Dr. Bhasker was also a member of NASA's High Performance Computer and Communications (HPCC) initiative that started the Information Superhighway project. He worked for four years with MDL Information Systems and Sybase Inc., California, USA and was the architect of the massively parallel DBMS, Sybase MPP. Prof. Bhasker was conferred the "Best Professor of Information Technology" at the AsianBrand Summit and Dewang Mehta Business School Awards (Sept. 2008). In 2004, He was honored as the "Best Researcher" in the field of Electronic Commerce by the McMaster World Congress, Hamilton, Canada. In 2001 Rotary Club awarded him "Scroll of Honor" for his work and public service in the field of Information Technology and Management. Prof. Bhasker serves in several advisory committees of Indian Government, is an honorary expert of National Science Education and Research Council of Canada, and a member of the Governing Boards of several engineering and management institutes. He has also served in several IT task forces and committees of the UP Government. He has to his credit several publications in the area of Data Management, Electronic Commerce, Agent Technologies and Data Mining in International Journals and Conferences. He has recently authored a book Electronic Commerce: Framework, Technologies and Applications published by Tata McGraw-Hill. K Srikumar Srikumar Krishnamoorthy is Senior Research Scientist at Infosys Technologies Limited. Currently, he is responsible for a product development initiative in the real-time and ondemand data virtualization space. He has around 8.5 years of industry experience in research, product development and consulting. He holds a Doctorate in IT & Systems Management from IIM-Lucknow. He has published several research papers in peer-reviewed conferences and journals. His key research interests include personalization in e-commerce, data federation, query optimization, information retrieval and machine learning. | |
| Table of contents | |
1. Recommender Systems-An Introduction 2. Collaborative Filtering 3. Recommender Systems-Data Mining Techniques 4. Recommender Systems-Association Rule Mining 5. Information Retrieval and Hybrid Methods for Recommender Systems 6. Design of Recommender Systems for e-Commerce 7. Recommender Systems for High-Involvement Products in e-Commerce 8. Applications of Recommender Systems | |
