Abstract
Among the applications of Web 2.0, social networking sites continue to proliferate and the volume of content keeps growing; as a result, information overload causes difficulty for users attempting to choose useful and relevant information. In this work, we propose a novel recommendation method based on different types of influences: social, interest and popularity, using personal tendencies in regard to these three decision factors to recommend photos in a photo-sharing website, Flickr. Because these influences have different degrees of impact on each user, the personal tendencies related to these three influences are regarded as personalized weights; combining influence scores enables predicting the scores of items. The experimental results show that our proposed methods can improve the quality of recommendations.
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Lai, CH., Liu, DR., Liu, ML. (2013). Recommendations Based on Different Aspects of Influences in Social Media. In: Huemer, C., Lops, P. (eds) E-Commerce and Web Technologies. EC-Web 2013. Lecture Notes in Business Information Processing, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39878-0_18
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DOI: https://doi.org/10.1007/978-3-642-39878-0_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39877-3
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