Advertisement

Learning to Rank Tweets with Author-Based Long Short-Term Memory Networks

  • Guangyuan PiaoEmail author
  • John G. Breslin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10845)

Abstract

Recommending tweets that a user might retweet plays an important role either in satisfying users’ information needs or in the dissemination of information in microblogging services such as Twitter. In this paper, we propose a deep neural network for tweet recommendations with author-based Long Short-Term Memory networks for learning the latent representations/embeddings of tweets. Our approach predicts the preference score of a tweet based on (1) the similarity between the embeddings of a user and the tweet, (2) the similarity between the embeddings of the user and the author (who posted the tweet). Despite its simplicity, we present that our approach can significantly outperform state-of-the-art methods with or without explicit features for recommending tweets in terms of five evaluation metrics.

References

  1. 1.
    Chen, K., Chen, T., Zheng, G., Jin, O., Yao, E., Yu, Y.: Collaborative personalized tweet recommendation. In: Proceedings of the 35th ACM SIGIR Conference, SIGIR 2012, pp. 661–670, vol. 800. ACM, Portland (2012)Google Scholar
  2. 2.
    Feng, W., Wang, J.: Retweet or not?: personalized tweet re-ranking. In: Proceedings of the Sixth ACM WSDM Conference, pp. 577–586. ACM (2013)Google Scholar
  3. 3.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997).  https://doi.org/10.1162/neco.1997.9.8.1735CrossRefGoogle Scholar
  4. 4.
    Hong, L., Doumith, A.S., Davison, B.D.: Co-factorization machines: modeling user interests and predicting individual decisions in Twitter. In: Proceedings of the Sixth ACM WSDM Conference, pp. 557–566. ACM (2013)Google Scholar
  5. 5.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  6. 6.
    Larochelle, H., Hinton, G.E.: Learning to combine foveal glimpses with a third-order Boltzmann machine. In: Lafferty, J.D., Williams, C.K.I., Shawe-Taylor, J., Zemel, R.S., Culotta, A. (eds.) Advances in Neural Information Processing Systems, vol. 23, pp. 1243–1251. Curran Associates, Inc. (2010)Google Scholar
  7. 7.
    LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., Jackel, L.D.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems, pp. 396–404 (1990)Google Scholar
  8. 8.
    Li, J., Xu, H., He, X., Deng, J., Sun, X.: Tweet modeling with LSTM recurrent neural networks for hashtag recommendation. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 1570–1577. IEEE, July 2016Google Scholar
  9. 9.
    Piao, G., Breslin, J.G.: Analyzing aggregated semantics-enabled user modeling on Google+ and Twitter for personalized link recommendations. In: User Modeling, Adaptation, and Personalization, pp. 105–109. ACM, Halifax (2016)Google Scholar
  10. 10.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)Google Scholar
  11. 11.
    Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be Retweeted? Large scale analytics on factors impacting retweet in Twitter network. In: 2010 IEEE Second International Conference on Social Computing, pp. 177–184. IEEE, August 2010Google Scholar
  13. 13.
    Zhang, Q., Gong, Y., Wu, J., Huang, H., Huang, X.: Retweet prediction with attention-based deep neural network. In: Proceedings of the 25th ACM CIKM 2016, pp. 75–84. ACM (2016)Google Scholar
  14. 14.
    Zhang, S., Yao, L., Sun, A.: Deep learning based recommender system: a survey and new perspectives. CoRR abs/1707.0 (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Insight Centre for Data AnalyticsNational University of IrelandGalwayIreland

Personalised recommendations