Abstract
As the sheer volume of available micro-videos often undermines the users’ capability to choose the micro-videos, in this paper, we propose a multi-source multi-net micro-video recommendation model that recommends micro-videos fitting users’ best interests. Different from existing works, as micro-video inherits the characteristics of social platforms, we simultaneously incorporate multi-source content data of items and multi-networks of users to learn user and item representations for recommendation. This information can be complementary to each other in a way that multi-modality data can bridge the semantic gap among items, while multi-type user networks, such as following and reposting, are able to propagate the preferences among users. Furthermore, to discover the hidden categories of micro-videos that properly match users’ interests, we interactively learn the user-item representations. The resulted categorical representations are interacted with user representations to model user preferences at different level of hierarchies. Finally, multi-source content item data, multi-type user networks and hidden item categories are jointly modelled in a unified recommender, and the parameters of the model are collaboratively learned to boost the recommendation performance. Experiments on a real dataset demonstrate the effectiveness of the proposed model and its advantage over the state-of-the-art baselines.
J. Ma and J. Wen are contributed equally to this work.
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
Notes
References
Cao, C., Ge, H., Lu, H., Hu, X., Caverlee, J.: What are you known for?: Learning user topical profiles with implicit and explicit footprints. In: SIGIR (2017)
Chang, S., Han, W., Tang, J., Qi, G.J., Aggarwal, C.C., Huang, T.S.: Heterogeneous network embedding via deep architectures. In: KDD (2015)
Chen, W., Wang, S., Long, G., Yao, L., Sheng, Q.Z., Li, X.: Dynamic illness severity prediction via multi-task rnns for intensive care unit. In: ICDM (2018)
Chen, X., Qin, Z., Zhang, Y., Xu, T.: Learning to rank features for recommendation over multiple categories. In: SIGIR (2016)
Chen, X., Zhang, Y., Ai, Q., Xu, H., Yan, J., Qin, Z.: Personalized key frame recommendation. In: Proceedings of the 40th International ACM SIGIR (2017)
Guo, G., Zhang, J., Yorke-Smith, N.: TrustSVD: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings (2015)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: WWW (2017)
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: RecSys (2010)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv preprint: arXiv:1412.6980
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD (2008)
Li, P., Wang, Z., Ren, Z., Bing, L., Lam, W.: Neural rating regression with abstractive tips generation for recommendation. In: SIGIR (2017)
Li, S., Kawale, J., Fu, Y.: Deep collaborative filtering via marginalized denoising auto-encoder. In: CIKM (2015)
Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., Rui, Y.: GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: KDD (2014)
Liang, D., Charlin, L., McInerney, J., Blei, D.M.: Modeling user exposure in recommendation. In: WWW (2016)
Ma, J., Li, G., Zhong, M., Zhao, X., Zhu, L., Li, X.: LGA: latent genre aware micro-video recommendation on social media. MTAP 77(3), 2991–3008 (2018)
Manotumruksa, J., Macdonald, C., Ounis, I.: A deep recurrent collaborative filtering framework for venue recommendation. In: CIKM (2017)
Mei, T., Yang, B., Hua, X.S., Yang, L., Yang, S.Q., Li, S.: VideoReach: an online video recommendation system. In: SIGIR (2007)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, WWW 2001 (2001)
Wang, S., Chang, X., Li, X., Sheng, Q.Z., Chen, W.: Multi-task support vector machines for feature selection with shared knowledge discovery. Signal Process. 120, 746–753 (2016)
Wang, S., Wang, Y., Tang, J., Shu, K., Ranganath, S., Liu, H.: What your images reveal: exploiting visual contents for point-of-interest recommendation. In: WWW (2017)
Wang, X., He, X., Nie, L., Chua, T.S.: Item silk road: recommending items from information domains to social users (2017). arXiv preprint: arXiv:1706.03205
Wen, J., Ma, J., Feng, Y., Zhong, M.: Hybrid attentive answer selection in CQA with deep users modelling. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Wu, Y., DuBois, C., Zheng, A.X., Ester, M.: Collaborative denoising auto-encoders for top-n recommender systems. In: WSDM (2016)
Xu, L., Wei, X., Cao, J., Yu, P.S.: Embedding of embedding (EOE): joint embedding for coupled heterogeneous networks. In: WSDM (2017)
Yang, C., Bai, L., Zhang, C., Yuan, Q., Han, J.: Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation. In: KDD (2017)
Yu, P.S., Yu, P.S., Yu, P.S.: Joint deep modeling of users and items using reviews for recommendation. In: WSDM (2017)
Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y.: Collaborative knowledge base embedding for recommender systems. In: KDD (2016)
Zhang, Y., Ai, Q., Chen, X., Croft, W.: Joint representation learning for top-n recommendation with heterogeneous information sources. In: CIKM (2017)
Zheng, L., Noroozi, V., Yu, P.S.: Joint deep modeling of users and items using reviews for recommendation. In: WSDM (2017)
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
Ma, J., Wen, J., Zhong, M., Chen, W., Zhou, X., Indulska, J. (2019). Multi-source Multi-net Micro-video Recommendation with Hidden Item Category Discovery. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11447. Springer, Cham. https://doi.org/10.1007/978-3-030-18579-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-18579-4_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18578-7
Online ISBN: 978-3-030-18579-4
eBook Packages: Computer ScienceComputer Science (R0)