Abstract
The on-demand cinemas, which has emerged in recent years, provide offline entertainment venues for individuals and small groups. Because of the limitation of network speed and storage space, it is necessary to recommend movies to cinemas, that is, to suggest cinemas to download the recommended movies in advance. This is particularly true for new cinemas. For the new cinema cold-start recommendation, we build a matrix factorization framework and then fuse location categories of cinemas and co-popular relationship between movies in the framework. Specifically, location categories of cinemas are learned through LDA from the type information of POIs around the cinemas and used to approximate cinema latent representations. Moreover, a SPPMI matrix that reflects co-popular relationship between movies is constructed and factorized collectively with the interaction matrix by sharing the same item latent representations. Extensive experiments on real-world data are conducted. The experimental results show that the proposed approach yields significant improvements over state-of-the-art cold-start recommenders in terms of precision, recall and NDCG.
This work was supported by the National Natural Science Foundation of China (No. 61472408) and the joint project with iQIYI (No. LUM18-200032).
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
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Cao, D., Nie, L., He, X., Wei, X., Zhu, S., Chua, T.S.: Embedding factorization models for jointly recommending items and user generated lists. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 585–594. ACM (2017)
Gantner, Z., Drumond, L., Freudenthaler, C., Rendle, S., Schmidt-Thieme, L.: Learning attribute-to-feature mappings for cold-start recommendations. In: 2010 IEEE International Conference on Data Mining, pp. 176–185 (2010)
Grbovic, M., et al.: E-commerce in your inbox: product recommendations at scale. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1809–1818. ACM (2015)
He, X., Zhang, H., Kan, M.Y., Chua, T.S.: Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 549–558. ACM (2016)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434. ACM (2008)
Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)
Levy, O., Goldberg, Y.: Neural word embedding as implicit matrix factorization. Adv. Neural Inf. Process. Syst. 3, 2177–2185 (2014)
Li, J., Lu, K., Huang, Z., Shen, H.T.: Two birds one stone: on both cold-start and long-tail recommendation. In: ACM on Multimedia Conference (2017)
Liang, D., Altosaar, J., Charlin, L., Blei, D.M.: Factorization meets the item embedding: regularizing matrix factorization with item co-occurrence. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 59–66. ACM (2016)
Peng, F., et al.: MetricRec: metric learning for cold-start recommendations. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q.Z. (eds.) ADMA 2016. LNCS (LNAI), vol. 10086, pp. 445–458. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49586-6_30
Sedhain, S., Menon, A.K., Sanner, S., Xie, L., Braziunas, D.: Low-rank linear coldstart recommendation from social data. In: AAAI, pp. 1502–1508 (2017)
Shi, L., Zhao, W.X., Shen, Y.: Local representative-based matrix factorization for cold-start recommendation. ACM Trans. Inf. Syst. 36(2), 22 (2017)
Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 650–658 (2008)
Suvash, S., Scott, S., Darius, B., Lexing, X., Jordan, C.: Social collaborative filtering for cold-start recommendations. In: Proceedings of the ACM Conference on Recommender Systems, vol. 14, RecSys (2014)
Tran, T., Lee, K., Liao, Y., Lee, D.: Regularizing matrix factorization with user and item embeddings for recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 687–696. ACM (2018)
Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 448–456. ACM (2011)
Xie, M., Yin, H., Wang, H., Xu, F., Chen, W., Wang, S.: Learning graph-based poi embedding for location-based recommendation. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 15–24. ACM (2016)
Xue, T., Jin, B., Li, B., Liu, K., Zhang, Q., Tian, S.: Spatial-temporal recommendation for on-demand cinemas. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) DASFAA 2019. LNCS, vol. 11448, pp. 373–377. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18590-9_48
Yu, J., Gao, M., Li, J., Yin, H., Liu, H.: Adaptive implicit friends identification over heterogeneous network for social recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 357–366. ACM (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, B., Jin, B., Xue, T., Liu, K., Zhang, Q., Tian, S. (2020). Cold-Start Recommendation for On-Demand Cinemas. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science(), vol 11908. Springer, Cham. https://doi.org/10.1007/978-3-030-46133-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-46133-1_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46132-4
Online ISBN: 978-3-030-46133-1
eBook Packages: Computer ScienceComputer Science (R0)
