Abstract
To improve the quality of the recommendation of the recommendation system, a distance-interest affective model is proposed to combine user location context on the preferences of user interests. Based on the model and user-based collaborative filtering algorithm, the location context aware collective filtering algorithm is designed. Firstly, measure the location-similarity between users through the user’s location context information. Second, calculate the origin user-similarity from the user-item rating matrix. Then, gain the location-similarity as a weight of final user similarity, calculate the final similarity. Finally, recommendation is supplied by top-N recommendation. The simulation results were compared with the traditional algorithm to prove the precision and recall rate of the proposed algorithm is superior to traditional algorithms.
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Yue, W., Song, M., Han, J., E, H. (2013). Location Context Aware Collective Filtering Algorithm. In: Zu, Q., Hu, B., Elçi, A. (eds) Pervasive Computing and the Networked World. ICPCA/SWS 2012. Lecture Notes in Computer Science, vol 7719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37015-1_69
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DOI: https://doi.org/10.1007/978-3-642-37015-1_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37014-4
Online ISBN: 978-3-642-37015-1
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