原标题:使用深度学习构建先进推荐系统:近期33篇重要研究概述

使用深度学习构建先进推荐系统:近期33篇重要研究概述-汇美优普

使用深度学习构建先进推荐系统:近期33篇重要研究概述-汇美优普

使用深度学习构建先进推荐系统:近期33篇重要研究概述-汇美优普

[2] A. Singhal, R. Kasturi, and J. Srivastava, “DataGopher: Context-based search for research datasets,” in Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014, 2014, pp. 749–756.

[3] A. Singhal, “Leveraging open source web resources to improve retrieval of low text content items,” ProQuest Diss. Theses, p. 161, 2014.

[4] A. Singhal, R. Kasturi, V. Sivakumar, and J. Srivastava, “Leveraging Web intelligence for finding interestingresearch datasets,” in Proceedings - 2013 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2013, 2013, vol. 1, pp. 321–328.

[5] J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, “Recommender system application developments: A survey,” Decis. Support Syst., vol. 74, pp. 12–32, 2015.

[6] S. Lakshmi and T. Lakshmi, “Recommendation Systems: Issues and challenges,” Int. J. Comput. Sci. Inf. Technol., vol. 5, no. 4, pp. 5771–5772, 2014.

[7] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

[8] A. van den Oord, S. Dieleman, and B. Schrauwen, “Deep content-based music recommendation,” Electron. Inf. Syst. Dep., p. 9, 2013.

[9] X. Wang and Y. Wang, “Improving Content-based and Hybrid Music Recommendation using Deep Learning,” MM, pp. 627–636, 2014.

[10] J. Tan, X. Wan, and J. Xiao, “A Neural Network Approach to Quote Recommendation in Writings,” Proc. 25th ACM Int. Conf. Inf. Knowl. Manag. - CIKM ’16, pp. 65–74, 2016.

[11] H. Lee, Y. Ahn, H. Lee, S. Ha, and S. Lee, “Quote Recommendation in Dialogue using Deep Neural Network,” in Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR ’16, 2016, pp. 957–960.

[12] T. Bansal, D. Belanger, and A. McCallum, “Ask the GRU,” in Proceedings of the 10th ACM Conference on Recommender Systems - RecSys ’16, 2016, pp. 107– 114.

[13] L. Zheng, V. Noroozi, and P. S. Yu, “Joint Deep Modeling of Users and Items Using Reviews for Recommendation,” 2017.

[14] X. Wang et al., “Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors’ Demonstration,” in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’17, 2017, pp. 2051– 2059.

[15] H. Wang, N. Wang, and D.-Y. Yeung, “Collaborative Deep Learning for Recommender Systems,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 1235–1244.

[16] S. Li, J. Kawale, and Y. Fu, “Deep Collaborative Filtering via Marginalized Denoising Auto-encoder,” in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM ’15, 2015, pp. 811–820.

[17] R. Devooght and H. Bersini, “Collaborative Filtering with Recurrent Neural Networks,” Aug. 2016.

[18] A. K. Balazs Hidasi, “Session-based Recommendation with Recurrent Neural Networks,” ICLR, pp. 1–10, 2016.

[19] B. Hidasi, M. Quadrana, A. Karatzoglou, and D. Tikk, “Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations,” in Proceedings of the 10th ACM Conference on Recommender Systems - RecSys ’16, 2016, pp. 241–248.

[20] D. Jannach and M. Ludewig, “When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation,” in Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys ’17, 2017, pp. 306–310.

[21] H.-J. Xue, X.-Y. Dai, J. Zhang, S. Huang, and J. Chen, “Deep Matrix Factorization Models for Recommender Systems *,” 2017.

[22] T. Ebesu and Y. Fang, “Neural Semantic Personalized Ranking for item cold-start recommendation,” Inf. Retr. J., vol. 20, no. 2, pp. 109–131, 2017.

[23] S. Cao, N. Yang, and Z. Liu, “Online news recommender based on stacked auto-encoder,” in Proceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017, 2017, pp. 721–726.

[24] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural Collaborative Filtering,” in Proceedings of the 26th International Conference on World Wide Web - WWW ’17, 2017, pp. 173–182.

[25] R. Van Den Berg, T. N. Kipf, and M. Welling, “Graph Convolutional Matrix Completion,” arXiv, 2017.

[26] Y. Wu, C. DuBois, A. X. Zheng, and M. Ester, “Collaborative Denoising Auto-Encoders for Top-N Recommender Systems,” in Proceedings of the Ninth ACM International Conference on Web Search and Data Mining - WSDM ’16, 2016, pp. 153–162.

[27] G. Sottocornola, F. Stella, M. Zanker, and F. Canonaco, “Towards a deep learning model for hybrid recommendation,” in Proceedings of the International Conference on Web Intelligence - WI ’17, 2017, pp. 1260–1264.

[28] X. Dong, L. Yu, Z. Wu, Y. Sun, L. Yuan, and F. Zhang, “A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems,” Aaai, pp. 1309– 1315, 2017.

[29] D. Kim, C. Park, J. Oh, and H. Yu, “Deep hybrid recommender systems via exploiting document context and statistics of items,” Inf. Sci. (Ny)., vol. 417, pp. 72– 87, 2017.

[30] H. Liang and T. Baldwin, “A Probabilistic Rating Autoencoder for Personalized Recommender Systems,” in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM ’15, 2015, pp. 1863–1866.

[31] S. P. Chatzis, P. Christodoulou, and A. S. Andreou, “Recurrent Latent Variable Networks for Session-Based Recommendation,” in Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems - DLRS 2017, 2017, pp. 38–45.

[32] V. Bogina and T. Kuflik, “Incorporating dwell time in session-based recommendations with recurrent Neural networks,” in CEUR Workshop Proceedings, 2017, vol. 1922, pp. 57–59.

[33] D. Kim, C. Park, J. Oh, S. Lee, and H. Yu, “Convolutional Matrix Factorization for Document Context-Aware Recommendation,” in Proceedings of the 10th ACM Conference on Recommender Systems - RecSys ’16, 2016, pp. 233–240.

[34] V. Kumar, D. Khattar, S. Gupta, and M. Gupta, “Deep Neural Architecture for News Recommendation,” in Working Notes of the 8th International Conference of the CLEF Initiative, Dublin, Ireland. CEUR Workshop Proceedings, 2017.

[35] S. Deng, L. Huang, G. Xu, X. Wu, and Z. Wu, “On Deep Learning for Trust-Aware Recommendations in Social Networks,” IEEE Trans. Neural Networks Learn. Syst., vol. 28, no. 5, pp. 1164–1177, 2017.

[36] D. Ding, M. Zhang, S.-Y. Li, J. Tang, X. Chen, and Z.-H. Zhou, “BayDNN: Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network,” in Conference on Information and Knowledge Management (CIKM), 2017, pp. 1479–1488.

[37] Z. Xu, C. Chen, T. Lukasiewicz, and Y. Miao, “Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation,” Aug. 2017.

[38] B. Bai, Y. Fan, W. Tan, and J. Zhang, “DLTSR: A Deep Learning Framework for Recommendation of Long-tail Web Services,” IEEE Trans. Serv. Comput., pp. 1–1, 2017.

[39] H. Soh, S. Sanner, M. White, and G. Jamieson, “Deep Sequential Recommendation for Personalized Adaptive User Interfaces,” in Proceedings of the 22nd International Conference on Intelligent User Interfaces - IUI ’17, 2017, pp. 589–593.

[40] Z. Xu, C. Chen, T. Lukasiewicz, Y. Miao, and X. Meng, “Tag-Aware Personalized Recommendation Using a Deep-Semantic Similarity Model with Negative Sampling,” in Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM ’16, 2016, pp. 1921– 1924.

机器之心推出「Synced Machine Intelligence Awards」2017,希望通过四大奖项记录这一年人工智能的发展与进步,传递行业启示性价值。

使用深度学习构建先进推荐系统:近期33篇重要研究概述-汇美优普