Abstract
Currently, a variety of eBooks with some intelligent capabilities to store and read digital books have been developed. With the use of these devices is easier to interact with the content available on the Web. But in some way access to such content is limited due to data overload problems. Trying to resolve this problem have been developed some techniques for information retrieval, among which are the recommender systems. These systems attempt to measure the taste and interest of users for some content and provide information relating to your profile. Through the feedback process attempts to collect the information that a recommendation system needs to work; but often this process requires the direct intervention of users, so that sometimes it is tedious and uncomfortable for users. For what we believe is necessary for a recommender system should be able to capture and measure implicitly the interaction parameters of a user with content in an eBook. Considering this need, we present a series of parameters that can be measured implicitly and how they will measured in the feedback process so that a recommender system to be reliable in electronic books.
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References
Sanjuan, O.: Using Recommendation System for E-learning Environments at degree level. International Journal of Artificial Intelligence and Interactive Multimedia 1(2) (2009) ISSN - 1989-1660
Taghipour, N., Kardan, A.: A Hybrid Web Recommender System Based on Q-Learning. In: SAC 2008: Proceedings of the 2008 ACM symposium on Applied computing, Fortaleza, Ceara, Brazil, pp. 1164–1168 (2008) ISBN:978-1-59593-753-7
O’Donovan, J., Smyth, B.: Trust in recommender systems. In: Proceedings of the 10th international conference on Intelligent user interfaces, pp. 167–174. ACM, San Diego (2005) ISBN:1-58113-894-6
Noor, S., Martinez, K.: Using Social Data as Context for Making Recommendations: An Ontology based Approach. In: ACM International Conference Proceeding Series, Proceedings of the 1st Workshop on Context, Information and Ontologies, Heraklion, Greece. ACM, New York (2005) ISBN:978-1-60558-528-4
Wang, P.: Why recommendation is special? In: Papers from the 1998 Workshop on Recommender Systems, part of the 15th National Conference on Artificial Intelligence (AAAI 1998), Madison, Wisconsin, EUA, pp. 111–113 (1998)
Gonzalez, R., Sanjuan, O., Cueva, J.M.: Recommendation System based on user interaction data applied to intelligent electronic books (2010)
Resnick, P., Varian, H.R.: Recommnder Systems. Communications of the ACM, 56–58 (1997)
Adonomavicius, G., Sen, S., Tuzhilin: A. Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach. ACM Transactions on Information Systems (TOIS) 23(1), 103–145 (2005)
Ziegler, C.-N., et al.: Improving Recommendation Lists Through Topic Diversification. In: International World Wide Web Conference. Proceedings of the 14th international conference on World Wide Web, Chiba, Japan, pp. 22–32. ACM, New York (2005) ISBN:1-59593-046-9
Nielsen, J.: Usability Metrics: Tracking Interface Improvements. IEEE Software 13(6), 12–13 (1996)
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Núñez V, E.R., Martínez, O.S., Lovelle, J.M.C., García-Bustelo, B.C.P. (2010). First Steps towards Implicit Feedback for Recommender Systems in Electronic Books. In: de Leon F. de Carvalho, A.P., Rodríguez-González, S., De Paz Santana, J.F., Rodríguez, J.M.C. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14883-5_8
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DOI: https://doi.org/10.1007/978-3-642-14883-5_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14882-8
Online ISBN: 978-3-642-14883-5
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