Abstract
The recommendation system can recommend information to users personally and efficiently, which satisfies the user’s demand for information in the information age, and has become a hot topic in the current era. In the recommendation system, users and items and the interaction of their own information has a crucial impact on the efficiency and accuracy of the recommendations. However, most of the existing recommendation systems usually design the systems as user-base only, considering the user’s influence on the item in the recommendation, which to some extent blurs the interaction between items and users at the item level, unknown and potential connections between items and users are not well considered. In this paper, we propose a collaborative memory network that can focus on the potential relation between items and users, and consider the impact of items’ characteristics on user behavior. Experiments have shown that our improvement is better than the original method and other baseline models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: 14th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pp. 426–434. ACM, Las Vegas (2008)
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_1
Tay, Y., Tuan, L.A., Hui, S.C.: Latent relational metric learning via memory-based attention for collaborative ranking. In: 27th International Conference on World Wide Web, pp. 729–739. ACM, Lyon (2018)
Ebesu, T., Shen, B., Fang, Y.: Collaborative memory network for recommendation systems. In: 41st ACM SIGIR International Conference on Research & Development in Information Retrieval, pp. 515–524. ACM, Ann Arbor (2018)
Subramani, S., Wang, H., Vu, H.Q., Li, G.: Domestic violence crisis identification from facebook posts based on deep learning. IEEE Access 6, 54075–54085 (2018)
Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: 32nd International Conference on Machine Learning, pp. 2048–2057. ACM, Lille (2015)
Luong, M.-T., Pham, H., Christopher, D.: Manning effective approaches to attention-based neural machine translation. In: 12th International Conference on Empirical Methods in Natural Language Processing, pp. 1412–1421. ACL, Lisbon (2015)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Amato, G., Carrara, F., Falchi, F., Gennaro, C.: Efficient indexing of regional maximum activations of convolutions using full-text search engines. In: 7th International Conference on Multimedia Retrieval, pp. 420–423. ACM, Bucharest (2017)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: 26th International Conference on World Wide Web, pp. 173–182. ACM, Perth (2017)
Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: 17th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pp. 448–456. ACM, San Diego (2011)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: 25th International Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI, Montreal (2009)
Wu, Y., DuBois, C., Zheng, A.X., Ester, M.: Collaborative denoising auto-encoders for top-N recommender systems. In: 9th ACM International Conference on Web Search and Data Mining, pp. 153–162. ACM, San Francisco (2016)
Kabbur, S., Ning, X., Karypis, G.: FISM: factored item similarity models for top-n recommender systems. In: 19th ACM SIGKDD International Conference on Knowledge discovery and Data Mining, pp. 659–667. ACM, Chicago (2013)
Acknowledgment
This work is supported in part by the National Natural Science Foundation of China (No. 61877043) and the National Natural Science Foundation of China (No. 61877044).
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
Yu, M. et al. (2019). Paper Recommendation with Item-Level Collaborative Memory Network. 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_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-29551-6_13
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)