| 1. |
Huang L, Lin C J, He J, et al. Diversified mobile application recommendation combining topic models and collaborative filtering. Journal of Software, 2017, 28(3):708-720 (in Chinese)
|
| 2. |
Liu J J. Research on fusion recommendation method based on content and collaborative filtering. Master Thesis. Hohhot, China: Inner Mongolia Teaching University, 2019 (in Chinese)
|
| 3. |
Zhao Q, Shi Y, Hong L J. Gb-cent: Gradient boosted categorical embedding and numerical trees. Proceedings of the 26th International Conference on World Wide Web (WWW'17), 2017, Apr 3-7, Perth, Australia. New York, NY, USA: ACM, 2017: 1311-1319
|
| 4. |
Juan Y, Lefortier D, Chapelle O. Field-aware factorization machines in a real-world online advertising system. Proceedings of the 26th International Conference on World Wide Web Companion (WWW'17 Companion), 2017, Apr 3-7, Perth, Australia. New York, NY, USA: ACM, 2017: 680-688
|
| 5. |
Cheng H T, Koc L, Harmsen J, et al. Wide & deep learning for recommender systems. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (DLRS’16), 2016, Sept 15, Boston, MA, USA. New York, NY, USA: ACM, 2016: 7-10
|
| 6. |
Wang X, He X N, Feng F L, et al. Tem: Tree-enhanced embedding model for explainable recommendation. Proceedings of the 2018 World Wide Web Conference (WWW’18), 2018, Apr 23-27, Lyon, France. New York, NY, USA: ACM, 2018: 1543-1552
|
| 7. |
Bobadilla J, Hernando A, Ortega F, et al. Collaborative filtering based on significances. Information Sciences, 2012, 185(1): 1-17
|
| 8. |
Zhu F, Yang J, Wang P F. TMRM: Two-stage multi-task recommendation model boosted feature selection. Proceedings of the 31st International Conference on Tools with Artificial Intelligence (ICTAI'19), 2019, Nov 4-6, Portland, OR, USA, Piscataway, NJ, USA: IEEE, 2019: 1277-1281.
|
| 9. |
Wang H, Shi X, Yeung D Y. Relational stacked denoising autoencoder for tag recommendation. Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI’15), Menlo Park, CA, USA: American Association for Artificial Intelligence (AAAI), 2015: 3052-3058
|
| 10 |
Guo H F, Tang R M, Ye Y M, et al. DeepFM: A factorization-machine based neural network for CTR prediction. Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI’17), 2017, Aug 19-25, Melbourne, Australia. Menlo Park, CA, USA: American Association for Artificial Intelligence (AAAI), 2017: 1725-1731
|
| 11 |
Wang R X, Fu B, Fu G, et al. Deep & cross network for ad click predictions. Proceedings of the 2017 AdKDD & Target Ad Workshop, 2017, Aug 13-17, Halifax, Canada. New York, NY, USA: ACM, 2017: 7p
|
| 12 |
Gao Y, Ran L X. Collaborative filtering recommendation algorithm for heterogeneous data mining in the Internet of things. IEEE Access, 2019, 7: 123583-123591
|
| 13 |
Xing S N, Liu F A, Wang Q Q, et al. Content-aware point-of-interest recommendation based on convolutional neural network. Applied Intelligence 2019, 49(2): 858-871
|
| 14 |
Sedhain S, Menon A K, Sanner S, et al. AutoRec: Autoencoders meet collaborative filtering. Proceedings of the 24th International Conference on World Wide Web (WWW'15), 2015, May 18-22, Florence, Italy. New York, NY, USA: ACM, 2015: 111-112
|
| 15 |
Strub F, Gaudel R, Mary J. Hybrid recommender system based on autoencoders. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (DLRS’16), 2016, Sept 15, Boston, MA, USA. New York, NY, USA: ACM, 2016: 11-16
|
| 16 |
Wu Y, DuBois C, Zheng A X, et al. Collaborative denoising auto-encoders for top-N recommender systems. Proceedings of the 9th ACM International Conference on Web Search and Data Mining (WSDM'16), 2016, Feb 22-25, San Francisco, CA, USA. New York, NY, USA: ACM, 2016: 153-162
|
| 17 |
Chen T Q, Guestrin C. Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16), 2016, Aug 13-17, San Francisco, CA, USA. New York, NY, USA: ACM, 2016: 785-794
|
| 18 |
Rendle S. Factorization machines. Proceedings of the 10th IEEE International Conference on Data Mining (ICDM’10), 2010, Dec 14-17, Sydney, Australia. Piscataway, NJ, USA: IEEE, 2010: 995-1000
|
| 19 |
Kendal A, Gal Y, Cipolla R. Multi-tasklearningusing uncertainty to weigh losses for scene geometry and semantics. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'18), 2018, Jun 18-22, Salt Lake City, UT, USA. Piscataway, NJ, USA: IEEE, 2018: 7482-7491
|
| 20 |
Hidasi B, Karatzoglou A. Recurrent neural networks with top-k gains for session-based recommendations. Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM’18), 2018, Oct 22-26, Turin, Italy. New York, NY, USA: ACM, 2018: 843-852
|
| 21 |
Cantador I, Brusilovsky P, Kuflik T. Second workshop on information heterogeneity and fusion in recommender systems (HetRec 2011). Proceedings of the 5th ACM Conference on Recommender Systems (RecSys’11), 2011, Oct 23-27, Chicago IL, USA. New York, NY, USA: ACM, 2011: 8p
|
