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Personalization techniques and recommender systems

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Published by World Scientific in Hackensack, NJ .
Written in English

Subjects:

  • Personal communication service systems.,
  • Recommender systems (Information filtering),
  • Wireless communication systems.

Book details:

Edition Notes

Includes bibliographical references and index.

Statementeditors, Gulden Uchyigit, Matthew Y. Ma.
SeriesSeries in machine perception and artificial intelligence -- v. 70
ContributionsUchyigit, G., Ma, M. Y.
Classifications
LC ClassificationsTK5103.485 .P47 2008
The Physical Object
Paginationx, 323 p. :
Number of Pages323
ID Numbers
Open LibraryOL17104639M
ISBN 109812797017
ISBN 109789812797018
LC Control Number2008298668

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Get this from a library! Personalization techniques and recommender systems. [G Uchyigit; Matthew Y Ma;] -- "This book is the first of its kind, representing research efforts in the diversity of personalization and recommendation techniques. These include user modeling, content, collaborative, hybrid and. This Special Issue on “Algorithms for Personalization Techniques and Recommender Systems” aims to form a reference point in this research area, i.e., the models and algorithms for the (more generic) goal of “personalization” and the (more specific) goal of “recommendations”. TY - BOOK. T1 - Personalization techniques and recommender systems. A2 - Uchyigit, Gulden. A2 - Ma, M.Y. PY - /4. Y1 - /4. N2 - The phenomenal growth of the Internet has resulted in huge amounts of online information, a situation that is overwhelming to the end by: A new approach to perform effective personalization based on Semantic web technologies achieved in a tutoring system is presented. This approach incorporates a recommender system based on collaborative tagging techniques that adapts to the interests and level of students' knowledge. These innovations are important contributions of this monograph.

==Content-Based Systems, Hybrid Systems and Machine Learning Methods: * Personalization Strategies and Semantic Reasoning: Working in Tandem in Advanced Recommender Systems (Y Blanco-Fernández et al.) * Content Classification and Recommendation Techniques for Viewing Electronic Programming Guide on a Portable Device (J Zhu et al.). Recommender systems are one technique for personalization; in essence the personalization occurs slowly as each system builds up information about your likes and dislikes, about what interests you and what fails to interest you. There are numerous other personalization techniques; most of these rely either on. Beside these common recommender systems, there are some specific recommendation techniques, as well. Specifically, context-aware recommender systems incorporate contex-tual information of users into the recommendation process (Verbert et al. ), tag-aware recommender systems integrate product tags to standard CF algorithms (Tso-Sutter et al. The book is the first of its kind, representing research efforts in the diversity of personalization and recommendation techniques. These include user modeling, content, collaborative, hybrid and knowledge-based recommender systems.

Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we . A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. They are primarily used in commercial applications.. Recommender systems are utilized in a variety of areas and are . The result shows how preference-inconsistent recommendations can be used for selection, elaboration, and evaluation of unbiased information selection. For making personalized paper recommendations, it is enough to match learner interests with paper topic. Multidimensional recommendation techniques are proposed in the eighth paper.   With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web. Key Features. Build industry-standard recommender systems; Only familiarity with Python is requiredReviews: