Abstract
Recently, friend recommendation has become an important application in a variety of social networking contexts, whether as part of in-house enterprise networks or as part of public networks like Twitter and Facebook. The value of these social networks is based, in part at least, on connecting the right people. But friend recommendation is challenging and many systems do little to help users make these valuable connections. In this paper, we build on previous work to consider new strategies for friend recommendation on Twitter. In particular, we compare strategies based on the content of users tweets, recommending users who tweet about similar things, to strategies based on Twitter-list tags by recommending users who are members of lists on similar topics. We describe a comprehensive evaluation to highlight the different benefits of these complementary strategies. We also discuss the most appropriate ways to evaluate their recommendations.
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© 2012 Springer-Verlag London
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Hannon, J., McCarthy, K., Smyth, B. (2012). Content vs. Tags for Friend Recommendation. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXIX. SGAI 2012. Springer, London. https://doi.org/10.1007/978-1-4471-4739-8_23
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DOI: https://doi.org/10.1007/978-1-4471-4739-8_23
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