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130626 ||| eng |
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|a 9781461447023
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| 100 |
1 |
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|a Yoo, Kyung-Hyan
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| 245 |
0 |
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|a Persuasive Recommender Systems
|h Elektronische Ressource
|b Conceptual Background and Implications
|c by Kyung-Hyan Yoo, Ulrike Gretzel, Markus Zanker
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| 250 |
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|a 1st ed. 2013
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| 260 |
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|a New York, NY
|b Springer New York
|c 2013, 2013
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| 300 |
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|a VI, 59 p. 9 illus
|b online resource
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| 505 |
0 |
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|a Introduction -- Theoretical Background -- Source Factors -- Message Factors -- Receiver and Context Factors -- Discussion -- Implications for Recommender System Design -- Directions for future research
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Data mining
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Data Mining and Knowledge Discovery
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| 700 |
1 |
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|a Gretzel, Ulrike
|e [author]
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| 700 |
1 |
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|a Zanker, Markus
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a SpringerBriefs in Electrical and Computer Engineering
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| 028 |
5 |
0 |
|a 10.1007/978-1-4614-4702-3
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| 856 |
4 |
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|u https://doi.org/10.1007/978-1-4614-4702-3?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3
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| 520 |
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|a Whether users are likely to accept the recommendations provided by a recommender system is of utmost importance to system designers and the marketers who implement them. By conceptualizing the advice seeking and giving relationship as a fundamentally social process, important avenues for understanding the persuasiveness of recommender systems open up. Specifically, research regarding influential factors in advice seeking relationships, which is abundant in the context of human-human relationships, can provide an important framework for identifying potential influence factors in recommender system context. This book reviews the existing literature on the factors in advice seeking relationships in the context of human-human, human-computer, and human-recommender system interactions. It concludes that many social cues that have been identified as influential in other contexts have yet to be implemented and tested with respect to recommender systems. Implications for recommender system research and design are discussed
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