Advances in Computing Applications by Amlan Chakrabarti, Neha Sharma, Visit Amazon's Valentina

By Amlan Chakrabarti, Neha Sharma, Visit Amazon's Valentina Emilia Balas Page, search results, Learn about Author Central, Valentina Emilia Balas,

This edited quantity provides the newest top quality technical contributions and study leads to the parts of computing, informatics, and data administration. The publication offers with state-of paintings subject matters, discussing demanding situations and attainable strategies, and explores destiny study instructions. the most target of this quantity is not just to summarize new examine findings but in addition position those within the context of earlier paintings. This quantity is designed for pro viewers, composed of researchers, practitioners, scientists and engineers in either the academia and the industry.

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http://www. packtpub. com/raspberry-pi-projects-for-kids/book

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Extra info for Advances in Computing Applications

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Int J Comput Sci Eng 8. Morales-del-Castillo JM, Peis E, Herrera-Viedma E (2010) A filtering and recommender system for e-scholars. Int J Technol Enhanc Learn 2(3):227–240 9. Pakdeetrakulwong U, Wongthongtham P (2013) State of the art of a multi-agent based recommender system for active software engineering ontology. Int J Digit Inf Wirel Commun (IJDIWC) 3(4):29–42 10. Bedi P, Vashisth P (2015) Argumentation-enabled interest-based personalised recommender system. J Exp Theor Artif Intell 27(2):199–226 11.

The evaluation of implementation on sample dataset is not included in the paper but it gives idea that agent-based information retrieval is useful [5]. BibTip is recommender system developed at Karlsruhe University. The architecture of BibTip consists of OPAC observation agent, aggregation agent, and recommendation agent. The co-occurrences between pair of titles are established if more titles are viewed in one session. The co-occurrences are summarized in co-occurrence matrix. The matrix is evaluated while generating recommendations.

BibTip, ExLibris bX, Techlens, Foxtrot, Fab, and LIBRA are listed as existing solutions to the field [2]. The workshop on new trends in content-based recommender systems (CBRecSys2014) is identified. The goal of CBRecSys2014 was to provide the platform for the papers dedicated to all aspects and new trends of content-based recommendations. There are many recommendation domains and applications where content and metadata play a key role. In domains such as movies, the relationship between content and usage data has seen thorough investigation done already but for many other domains such as books, news, scientific articles, and Web pages, it is not still known if and how these data sources should be combined to provide the best recommendation performance.

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