Download Advances in Recommender Systems (Smart Innovation, Systems by Lakhmi C. Jain, George A. Tsihrintzis, Maria Virvou PDF

By Lakhmi C. Jain, George A. Tsihrintzis, Maria Virvou

Multimedia companies at the moment are widely used in a variety of actions within the day-by-day lives of people. comparable program components comprise prone that let entry to massive depositories of knowledge, 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 providers, in addition to their cellular opposite numbers (i.e., m-services). regardless of the super progress of multimedia companies over the hot years, there's an expanding call for for his or her additional improvement. This call for is pushed by means of the ever-increasing hope of society for simple accessibility to details in pleasant, custom-made and adaptive environments.

In this publication to hand, we learn fresh Advances in Recommender platforms. Recommender structures are an important in multimedia companies, as they target at keeping the carrier clients from information overload. The publication contains 9 chapters, which current quite a few fresh study ends up in recommender systems.

This study ebook is directed to professors, researchers, software engineers and scholars of all disciplines who're attracted to studying extra approximately recommender platforms, advancing the corresponding state-of-the-art and constructing recommender structures for particular applications.

 

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Extra info for Advances in Recommender Systems (Smart Innovation, Systems and Technologies: Multimedia Services in Intelligent Environments Volume 24)

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J. 12(1), 3–13 (2006) 27. : Music recommendation from song sets. Presented at the Proceedings of 5th International Conference on Music Information Retrieval, pp. 425–428 (2004) 28. : Foafing the music: a music recommendation system based on RSS feeds and user preferences. In: Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR), London, UK (2005) 29. : Musiper: a system for modeling music similarity perception based on objective feature subset selection. User Model.

Part A: Syst. Humans 38(6), 1262–1272 (2008) 60. : Explaining collaborative filtering recommendations. In: CSCW ’00: Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work, pp. 241–250. ACM, New York (2000) 61. : Human-computer collaboration in recommender systems. In: Carroll, J. ) HCI in the New Millennium. Addison Wesley, Reading (2001) Hybrid User Model for Capturing a User’s Information Seeking Intent Hien Nguyen and Eugene Santos Jr. Abstract A user is an important factor that contributes to the success or failure of any information retrieval system.

Improve the user’s effectiveness. Specifically, we determine how frequent this tool has helped in the previous retrieval process. Currently, if the total number of retrieved relevant documents exceeds a user-defined threshold, the tool used for the query modification is considered helpful. 1 Overview We combine the user intent with the elements of an IR application in a decision theoretic framework to construct a hybrid model to improve a user’s effectiveness in a search. Our solution is to convert this problem into a multi-attribute decision problem and use multi-attribute utility theory [33] in which a set of attributes is constructed by combining the set of attributes describing a user’s intent and the set of attributes describing an IR system.

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