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Paper Recommendation with Item-Level Collaborative Memory Network

Published: 28 August 2019 Publication History

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.

References

[1]
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)
[2]
Ricci F, Rokach L, and Shapira B Ricci F, Rokach L, Shapira B, and Kantor PB Introduction to recommender systems handbook Recommender Systems Handbook 2011 Boston, MA Springer 1-35
[3]
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)
[4]
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)
[5]
Subramani S, Wang H, Vu HQ, and Li G Domestic violence crisis identification from facebook posts based on deep learning IEEE Access 2018 6 54075-54085
[6]
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)
[7]
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)
[8]
Goodfellow I, Bengio Y, and Courville A Deep Learning 2016 Cambridge MIT Press
[9]
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)
[10]
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)
[11]
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)
[12]
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)
[13]
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)
[14]
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)

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        cover image Guide Proceedings
        Knowledge Science, Engineering and Management: 12th International Conference, KSEM 2019, Athens, Greece, August 28–30, 2019, Proceedings, Part I
        Aug 2019
        867 pages
        ISBN:978-3-030-29550-9
        DOI:10.1007/978-3-030-29551-6

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        Springer-Verlag

        Berlin, Heidelberg

        Publication History

        Published: 28 August 2019

        Author Tags

        1. Recommendation systems
        2. Memory network
        3. Collaborative filtering

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