Abstract
Cold-start problem and sparse, long-tailed datasets are inevitable issues in recommendation systems. The solution to these problems is not to predict them in isolation, but to exploit the additional information from relevant activities. Hence recent sequential actions and social relationships of the user can be used to improve the effectiveness of the model. In this paper, we develop a novel approach called Socially-aware and sequential embedding (SASE) to fill the gap by leveraging convolutional filters to capture the sequential pattern and learning the individual social features from the social networks simultaneously. The core idea is to determine which item is relevant to the user’s historical actions and seek who is the user’s intimate friend, then make predictions based on these signals. Experimental results on several real-world datasets verify the superiority of our approach compared with various state-of-the-art baselines when handling the cold-start issues.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 811–820. ACM (2010)
Cai, C., He, R., McAuley, J.: SPMC: socially-aware personalized markov chains for sparse sequential recommendation. arXiv preprint arXiv:1708.04497 (2017)
Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 565–573. ACM (2018)
Kang, W.-C., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 197–206. IEEE (2018)
Ekstrand, M.D., Riedl, J.T., Konstan, J.A., et al.: Collaborative filtering recommender systems. Found. Trends® Hum.-Comput. Interact. 4(2), 81–173 (2011)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)
Zhao, T., McAuley, J., King, I.: Leveraging social connections to improve personalized ranking for collaborative filtering. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 261–270. ACM (2014)
Kabbur, S., Ning, X., Karypis, G.: FISM: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 659–667. ACM (2013)
Ning, X., Karypis, G.: SLIM: sparse linear methods for top-n recommender systems. In: 2011 IEEE 11th International Conference on Data Mining, pp. 497–506. IEEE (2011)
He, R., Kang, W.-C., McAuley, J.: Translation-based recommendation: a scalable method for modeling sequential behavior. In: IJCAI, pp. 5264–5268 (2018)
Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted boltzmann machines for collaborative filtering. In Proceedings of the 24th international conference on Machine learning, pp. 791–798. ACM (2007)
Zhou, C., et al.: ATRank: an attention-based user behavior modeling framework for recommendation. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Geng, X., Zhang, H., Bian, J., Chua, T.-S.: Learning image and user features for recommendation in social networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4274–4282 (2015)
Chaney, A.J.B., Blei, D.M., Eliassi-Rad, T.: A probabilistic model for using social networks in personalized item recommendation. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 43–50. ACM (2015)
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 135–142. ACM (2010)
Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 203–210. ACM (2009)
Tang, J., Gao, H., Hu, X., Liu, H.: Exploiting homophily effect for trust prediction. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 53–62. ACM (2013)
Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 931–940. ACM (2008)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM (2016)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Srivastava, N., Hinton, G., 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)
Tang, J., Gao, H., Liu, H.: mTrust: discerning multi-faceted trust in a connected world. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 93–102. ACM (2012)
Acknowledgement
This work was supported by NSFC (grant No. 61877051), CSTC (grant No. cstc2018jscx-msyb1042, cstc2017zdcy-zdyf0366 and cstc2017rgzn-zdyf0064). Li Li is the corresponding author for the paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Huang, K., Cao, Y., Du, Y., Li, L., Liu, L., Liao, J. (2019). Social-Aware and Sequential Embedding for Cold-Start Recommendation. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-29551-6_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29550-9
Online ISBN: 978-3-030-29551-6
eBook Packages: Computer ScienceComputer Science (R0)