Abstract
On the Twitter platform, an effective followee recommendation system is helpful to connecting users in a satisfactory manner. Topological relations and tweets content are two main factors considered in a followee recommendation system. However, how to combine these two kinds of information in a uniform framework is still an open problem. In this paper, we propose to combine deep learning techniques and collaborative information to explore the user representations latent behind the topology and content. Over two kinds of user representations (i.e., topology representation and content representation), we design an adaptive layer to dynamically leverage the contribution of topology and content to recommending followees, which changes the situation where the contribution weights are usually predefined. Experiments on a real-world Twitter dataset show that our proposed model provides more satisfying recommendation results than state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Armentano, G.M., Godoy, L.D., Analia, A.A.: A topology-based approach for followees recommendation in Twitter. In: 9th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (2011)
Bengio, Y., Schwenk, H., Senécal, J.S., Morin, F., Gauvain, J.L.: Neural probabilistic language models. In: Holmes, D.E., Jain, L.C. (eds.) Innovations in Machine Learning, vol. 194, pp. 137–186. Springer, Heidelberg (2006)
Bordes, A., Glorot, X., Weston, J., Bengio, Y.: A semantic matching energy function for learning with multi-relational data. Mach. Learn. 94(2), 233–259 (2014)
Bordes, A., Weston, J., Collobert, R., Bengio, Y., et al.: Learning structured embeddings of knowledge bases. In: Proceedings AAAI (2011)
Cai, X., Bain, M., Krzywicki, A., Wobcke, W., Kim, Y.S., Compton, P., Mahidadia, A.: Collaborative filtering for people to people recommendation in social networks. In: Li, J. (ed.) AI 2010. LNCS (LNAI), vol. 6464, pp. 476–485. Springer, Heidelberg (2010). doi:10.1007/978-3-642-17432-2_48
Chen, H., Cui, X., Hai, J.: Top-k followee recommendation over microblogging systems by exploiting diverse information sources. Future Comput. Syst. (2014)
Chen, T., Tang, L., Liu, Q., Yang, D., Xie, S., Cao, X., et al.: Combining factorization model and additive forest for collaborative followee recommendation. In: KDD-Cup Workshop (2012)
Dong, L., Wei, F., Zhou, M., Xu, K.: Adaptive multi-compositionality for recursive neural models with applications to sentiment analysis. In: Proceedings of AAAI, pp. 1537–1543 (2014)
Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of ICML, pp. 513–520 (2011)
Hannon, J., Bennett, M., Smyth, B.: Recommending Twitter users to follow using content and collaborative filtering approaches. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 199–206. ACM (2010)
He, Z., Liu, S., Li, M., Zhou, M., Zhang, L., Wang, H.: Learning entity representation for entity disambiguation. In: Proceedings of ACL, pp. 30–34 (2013)
Kim, Y., Shim, K.: TWITOBI: a recommendation system for Twitter using probabilistic modeling. In: Proceedings of ICDM, pp. 340–349. IEEE (2011)
Kim, Y., Shim, K.: TWILITE: a recommendation system for twitter using a probabilistic model based on latent dirichlet allocation. Inf. Syst. 42, 59–77 (2014)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of ACM SIGKDD, pp. 426–434 (2008)
Li, R., Wang, S., Chang, K.C.C.: Multiple location profiling for users and relationships from social network and content. Proc. VLDB Endow. 5(11), 1603–1614 (2012)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)
Lichtenwalter, R.N., Chawla, N.V.: LPmade: link prediction made easy. J. Mach. Learn. Res. 12, 2489–2492 (2011)
Lichtenwalter, R.N., Lussier, J.T., Chawla, N.V.: New perspectives and methods in link prediction. In: Proceedings of ACM SIGKDD, pp. 243–252. ACM (2010)
Ma, T., Yang, Y., Liangwei, W., Yuan, B.: Recommending people to follow using asymmetric factor models with social graphs. In: KDD-Cup Workshop (2012)
McPherson, M., Smith-Lovin, L., Cook, J.: Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27, 415–444 (2001)
Rendle, S.: Factorization machines. In: Proceedings of ICDM, pp. 995–1000 (2010)
Smith, M.R., Gashler, M.S., Martinez, T.: A hybrid latent variable neural network model for item recommendation. In: Proceedings of IJCNN, pp. 1–7 (2015)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Proceedings of NIPS, pp. 3104–3112 (2014)
Yu, Y., Qiu, G.R.: Followee recommendation in microblog using matrix factorization model with structural regularization. Sci. World J. (2014)
Acknowledgement
We thank all the anonymous reviewers for their insightful comments on this paper. This work was partially supported by National Natural Science Foundation of China (61273278 and 61572049). The correspondence author of this paper is Sujian Li.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Liu, Y., Chen, X., Li, S., Wang, L. (2016). A User Adaptive Model for Followee Recommendation on Twitter. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-50496-4_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50495-7
Online ISBN: 978-3-319-50496-4
eBook Packages: Computer ScienceComputer Science (R0)