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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References
Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press (2010)
Friedkin, N.E.: A structural theory of social influence. Cambridge University Press (1998)
Crandall, D., Cosley, D., Huttenlocher, D., Kleinberg, J., Suri, S.: Feedback effects between similarity and social influence in online communities. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 160–168. ACM, Las Vegas (2008)
He, J., Chu, W.W.: A social network-based recommender system (SNRS). In: Memon, N., Xu, J.J.J., Hicks, D.L.L., Chen, H. (eds.) Data Mining for Social Network Data, pp. 47–74. Springer US (2010)
Salton, G., Harman, D.: Information retrieval. John Wiley and Sons Ltd. (2003)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of the 2nd ACM Conference on Electronic Commerce, pp. 158–167. ACM, Minneapolis (2000)
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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
Online ISBN: 978-3-642-39878-0
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