Abstract
Due to the influence of different factors such as environment, age, and interest, everyone has a different taste and appreciation of the movie. These factors can be used to make more personalized recommendation calculations for movie recommendations, but many traditional methods do not integrate these factors well. Therefore, how to effectively extract key information from multiple directions in a recommendation system is still a challenging problem.
In this paper, we use user, film, and movie scoring data as input, and use the CNN-BLSTM deep learning model that incorporates the multi-head attention mechanism as a training, and finally combine the output features to calculate user preferences for recommendation. The convolutional neural network and LSTM are used to extract the user and movie feature information from the matrix, and the multi-head attention mechanism can also extract the key information from the data. The comparative experimental analysis of different models shows that our proposed user preference model based on attention mechanism can obtain better performance than traditional extraction methods.
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References
Socher, R., Huval., B., Manning, C.D., Ng, A.Y.: Semantic compositionality through recursive matrix-vector space. In: Conference on Empirical Methods in Natural Language Processing (2012)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1746–1751 (2014)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 1–19 (2009)
Zhang, Y., Zincir-Heywood, N., Milios, E.: World wide web site summarization. Web Intell. Agent Syst. Int. J. 2, 39–53 (2004)
Jones, S., Staveley, M.S.: Phrasier: a system for interactive document retrieval using keyphrases. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 160–167. ACM (1999)
Socher, R., Perelygin, A., Wu, J.Y., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, pp. 1631–1642 (2013)
Tang, D., Qin, B., Wei, F., et al.: A joint segmentation and classification framework for sentence level sentiment classification. IEEE/ACM Trans. Audio Speech Lang. Process. 23, 1750–1761 (2015)
Xie, R., Liu, Z., Sun, M.: Representation learning of knowledge graphs with hierarchical types, pp. 2659–2665 (2016)
Jacob, T.I.: A neural model for straight line detection in the human visual system. Dissert. Theses – Gradworks (2014)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Comput. Sci. (2014)
Ramirez-Moreno, D.F., Schwartz, O., Ramirez-Villegas, J.F.: A saliency-based bottom-up visual attention model for dynamic scenes analysis. Biol. Cybern. 107, 141–160 (2013)
Luong, M.-T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation (2015)
Xu, J., Chen, K., Qiu, X., et al.: Knowledge graph representation with jointly structural and textual encoding. 1318–1324 (2017)
Ding, Y., Li, X.: Time weight collaborative filtering. In: Proceedings of the 2005 ACM CIKM International Conference on Information and Knowledge Management, Bremen, Germany (2005)
Su, P., Ye, H.: [IEEE 2009 International Joint Conference on Artificial Intelligence (JCAI) - Hainan Island, China (2009.04.25–2009.04.26)] International Joint Conference on Artificial Intelligence - An Item Based Collaborative Filtering Recommendation Algorithm Usin, pp. 308–311 (2009)
Schafer, J.B., K., Konstan, J., Riedi, J.: [ACM Press the 1st ACM conference - Denver, Colorado, United States (1999.11.03–1999.11.05)] Proceedings of the 1st ACM conference on Electronic commerce, - EC 1999 - Recommender systems in e-commerce, pp. 158–166 (1999)
Cho, K., Van Merrienboer, B., Gulcehre, C., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014)
Cheng, H.T., Koc, L., Harmsen, J., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7–10. ACM (2016)
Zhu, J., Shan, Y., Mao, J., Yu, D., Rahmanian, H., Zhang, Y.: Deep embedding forest: Forest-based serving with deep embedding features. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, USA, pp. 13–17 (2017)
Collobert, R., Weston, J., Bottou, L., et al.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)
Liu, Y., Sun, C., Lin, L., et al.: Learning natural language inference using bidirectional lstm model and inner-attention (2016)
Huang, C., Kuo, P.-H.: A deep CNN-LSTM model for particulate matter (PM2.5) forecasting in smart cities. Sensors 18, 2220 (2018)
Wang, X., Yu, L., Ren, K., et al.: Dynamic attention deep model for article recommendation by learning human editors’ demonstration (2017)
Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)
Zhai, S., Chang, K.-H., Zhang, R., Zhang, Z.M.: Deepintent: learning attentions for online advertising with recurrent neural networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1295–1304 (2016)
Zhou, C., et al.: ATRank: an attention based user behavior modeling framework for recommendation. In: AAAI, pp. 4564–4571 (2018)
Vaswani, A., et al.: Attention is all you need. CoRR abs/1706.03762 (2017)
Dubuisson-Jolly, M.P., Gupta, A.: Color and texture fusion: application to aerial image segmentation and gis updating. Image Vis. Comput. 18, 823–832 (2000)
Mikolov, T., Sutskever, I., Chen, K., et al.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Zhaowei, Q., Cao, B., Wang, X., Li, F., Xu, P., Zhang, L.: Feedback LSTM network based on attention for image description generator. Comput. Mater. Continua 59(2), 575–589 (2019)
Feng, X., Zhang, X., Xin, Z., Yang, A.: Investigation on the Chinese text sentiment analysis based on convolutional neural networks in deep learning. Comput. Mater. Continua 58(3), 697–709 (2019)
Wang, G., Liu, M.: Dynamic trust model based on service recommendation in big data. Comput. Mater. Continua 58(3), 845–857 (2019)
Acknowledgement
The authors gratefully acknowledge support from National Key R&D Program of China (No. 2018YFC0831800), National Natural Science Foundation of China (No. 61872134), Natural Science Foundation of Hunan Province (No. 2018JJ2062), Science and technology development center of the Ministry of Education, and the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property, Universities of Hunan Province.
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Zhu, L., Liu, Y., Zhang, W., Yang, K. (2020). Research on User Preference Film Recommendation Based on Attention Mechanism. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12240. Springer, Cham. https://doi.org/10.1007/978-3-030-57881-7_38
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DOI: https://doi.org/10.1007/978-3-030-57881-7_38
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