Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (1): 118-129.doi: 10.23940/ijpe.20.01.p13.118129
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Chang Sua and Deling Huangab*()
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Accepted on
Contact:
Deling Huang
E-mail:huangdl@cqupt.edu.cn
Supported by:
Chang Su and Deling Huang. Hybrid Recommender System based on Deep Learning Model [J]. Int J Performability Eng, 2020, 16(1): 118-129.
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1. | J. Bobadilla, F. Ortega, A. Hernando,A. Gutiérrez, “Recommender Systems Survey,”Knowledge-based Systems, Vol. 46, pp. 109-132, 2013 |
2. | B. Cao, X. Liu, M. M. Rahman, B. Li, J. Liu,M. D. Tang, “Integrated Content and Network-based Service Clustering and Web APIs Recommendation for Mashup Development,”IEEE Transactions on Services Computing, 2017 |
3. | Q. Le and T. Mikolov, “Distributed Representations of Sentences and Documents,” inProceedings of International Conference on Machine Learning, pp. 1188-1196, 2014 |
4. | G. Ling, M. R. Lyu, King, and I. King, “Ratings Meet Reviews, a Combined Approach to Recommend,” inProceedings of the 8th ACM Conference on Recommender Systems, pp. 105-112, 2014 |
5. | W. Dou, X. Zhang,J. Chen, “Kasr: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Application,” IEEE Transactions on Parallel and Distributed Systems, Vol. 1, January 2014 |
6. | Y. Zhong, Y. Fan, W. Tan,J. Zhang, “Web Service Recommendation with Reconstructed Profile from Mashup Descriptions,” IEEE Transactions on Automation Science and Engineering, Vol. 15, No. 2, pp. 468-478, 2016 |
7. | L. Yao, X. Wang, Q. Z. Sheng, B. Benatallah,C. Huang, “Mashup Recommendation by Regularizing Matrix Factorization with API Co-Invocations,”IEEE Transactions on Services Computing, 2018 |
8. | C. Wang and D. M. Blei, “Collaborative Topic Modeling for Recommending Scientific Articles,” inProceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 448-456, 2011 |
9. | L. Yang, “Research on Hybrid Recommendation Algorithm based on Theme Model,” Ph.D. thesis, University of Electronic Science and Technology of China, 2014 |
10. | A. Mnih and R. R Salakhutdinov, “Probabilistic Matrix Factorization,” in: Advances in Neural Information Processing Systems, pp. 1257-1264, 2008 |
11. | D. Kim, C. Park, J. Oh,H. Yu, “Deep Hybrid Recommender Systems via Exploiting Document Context and Statistics of Items,”Information Sciences, Vol. 417, pp. 72-87, 2017 |
12. | H. Wang, N. Wang,D. Y. Yeung, “Collaborative Deep Learning for Recommender Systems,” inProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1235-1244, 2015 |
13. | Y. Koren, R. Bell,C. Volinsky, “ Matrix Factorization Techniques for Recommender Systems,”Computer, Vol. 8, pp. 30-37, 2009 |
14. | A. Van den Oord, S. Dieleman, and B. Schrauwen, “Deep Content-based Music Recommendation,” in: Advances in Neural Information Processing Systems, pp. 2643-2651, 2013 |
15. | J. McAuley, C. Targett, Q. Shi,A. Van Den Hengel, “Image-based Recommendations on Styles and Substitutes,” inProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43-52, 2015 |
16. | J. Zhou, R. Albatal,C. Gurrin, “Applying Visual User Interest Profiles for Recommendation and Personalisation,” inProceedings of International Conference on Multimedia Modeling, pp. 361-366, 2016 |
17. | T. Bansal, D. Belanger,A. McCallum, “Ask the Gru: Multi-Task Learning for Deep Text Recommendations,” inProceedings of the 10th ACM Conference on Recommender Systems, pp. 107-114, 2016 |
18. | P. Covington, J. Adams,E. Sargin, “Deep Neural Networks for Youtube Recommendations,” inProceedings of the 10th ACM Conference on Recommender Systems, pp. 191-198, 2016 |
19. | G. Zanotti, M. Horvath, L. N. Barbosa, V. T. K. G. Immedisetty,J. Gemmell, “Infusing Collaborative Recommenders with Distributed Representations,” inProceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 35-42, 2016 |
20. | K. Cho, B. Van Merriënboer, D. Bahdanau,Y. Bengio, “ On the Properties of Neural Machine Translation: Encoder-Decoder Approaches,” inProceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pp. 103-111, 2018 |
21. | J. Wei, J. He, K. Chen, Y. Zhou,Z. Tang, “Collaborative Filtering and Deep Learning based Recommendation System for Cold Start Items,” Expert Systems with Applications, Vol. 29-39+69, 2017 |
22. | P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio,P.A. Manzagol, “Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion,” Journal of Machine Learning Research, Vol. 11, pp. 3371-3408, December 2010 |
23. | Y. Koren, “Collaborative Filtering with Temporal Dynamics,” inProceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 447-456, 2009 |
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