By Lakhmi C. Jain, George A. Tsihrintzis, Maria Virvou
Multimedia companies at the moment are typical in a number of actions within the day-by-day lives of people. comparable program components comprise companies that let entry to massive depositories of data, electronic libraries, e-learning and e-education, e-government and e-governance, e-commerce and e-auctions, e-entertainment, e-health and e-medicine, and e-legal prone, in addition to their cellular opposite numbers (i.e., m-services). regardless of the large progress of multimedia providers over the new years, there's an expanding call for for his or her extra improvement. This call for is pushed by way of the ever-increasing wish of society for simple accessibility to info in pleasant, custom-made and adaptive environments.
In this publication to hand, we study contemporary Advances in Recommender structures. Recommender platforms are the most important in multimedia companies, as they target at conserving the carrier clients from information overload. The publication contains 9 chapters, which current quite a few fresh study leads to recommender systems.
This study publication is directed to professors, researchers, program engineers and scholars of all disciplines who're attracted to studying extra approximately recommender structures, advancing the corresponding cutting-edge and constructing recommender structures for particular applications.
Read or Download Advances in Recommender Systems (Smart Innovation, Systems and Technologies: Multimedia Services in Intelligent Environments Volume 24) PDF
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Extra info for Advances in Recommender Systems (Smart Innovation, Systems and Technologies: Multimedia Services in Intelligent Environments Volume 24)
In: Proceedings of the Fifth DELOS Workshop on Filtering and Collaborative Filtering, pp. 31–36 (1997) 14. : Newsweeder: learning to filter netnews. In: Proceedings of 12th International Machine Learning Conference (ML95), pp. 331–339 (1995) 28 A. S. Lampropoulos and G. A. Tsihrintzis 15. : Hybrid recommender systems: survey and experiments. User Model. User-Adapt. Interact. 12(4), 331–370 (2002) 16. : Content-based book recommending using learning for text categorization. In: DL ’00: Proceedings of the Fifth ACM Conference on Digital Libraries, pp.
The STRATIFIED model is constructed based on the assumption that the interactions between the user and the target IR system help the user’s information seeking tasks. Within the last decade, researchers also have explored a user’s searching behaviors for constructing a user model. These studies have shown that by understanding a user’s searching behaviors, we develop a more flexible IR system with personalized responses to an 36 H. Nguyen and E. Santos Jr. individual’s needs [18, 19, 21, 58, 67]; (Ruthven et al.
Individual’s needs [18, 19, 21, 58, 67]; (Ruthven et al. 2003). For example, in [18, 56], temporal factor, uncertainty, and partial assessment are combined to modify the weight of a term in a relevance feedback process. The main difference between the existing approaches which incorporate a user’s searching behaviors discussed above with our approach is that they use a user’s search behaviors to modify the weight of an individual term while ours uses the captured user intent to modify the relationships among terms in a query.