RECSYS PAPER REVIEW

Deep learning based recommender system: A survey and new perspectives.
Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). ACM computing surveys (CSUR), 52(1), 1-38.

Neural collaborative filtering.
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017, April). In Proceedings of the 26th international conference on world wide web (pp. 173-182).

Deep matrix factorization models for recommender systems.
Xue, H. J., Dai, X., Zhang, J., Huang, S., & Chen, J. (2017, August). In IJCAI (Vol. 17, pp. 3203-3209). 

Deepcf: A unified framework of representation learning and matching function learning in recommender system.
Deng, Z. H., Huang, L., Wang, C. D., Lai, J. H., & Yu, P. S. (2019, July). In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, No. 01, pp. 61-68). 

Joint neural collaborative filtering for recommender systems.
Chen, W., Cai, F., Chen, H., & Rijke, M. D. (2019). ACM Transactions on Information Systems (TOIS), 37(4), 1-30. 

Dual-embedding based neural collaborative filtering for recommender systems.
He, G., Zhao, D., & Ding, L. (2021). arXiv preprint arXiv:2102.02549. 

DDFL: a deep dual function learning-based model for recommender systems.
Shah, S. T. U., Li, J., Guo, Z., Li, G., & Zhou, Q. (2020, September). In International Conference on Database Systems for Advanced Applications (pp. 590-606). Cham: Springer International Publishing. 

Outer product-based neural collaborative filtering.
He, X., Du, X., Wang, X., Tian, F., Tang, J., & Chua, T. S. (2018). arXiv preprint arXiv:1808.03912. 

Comet: Convolutional dimension interaction for collaborative filtering.
Lin, Z., Feng, L., Guo, X., Zhang, Y., Yin, R., Kwoh, C. K., & Xu, C. (2023). ACM Transactions on Intelligent Systems and Technology, 14(4), 1-18. 

Deep collaborative recommendation algorithm based on attention mechanism.
Cui, C., Qin, J., & Ren, Q. (2022). Applied Sciences, 12(20), 10594. 

Dual relations network for collaborative filtering.
Ji, D., Xiang, Z., & Li, Y. (2020). IEEE Access, 8, 109747-109757. 

DELF: A dual-embedding based deep latent factor model for recommendation.
Cheng, W., Shen, Y., Zhu, Y., & Huang, L. (2018, July). In IJCAI (Vol. 18, pp. 3329-3335). 

Slim: Sparse linear methods for top-n recommender systems.
Ning, X., & Karypis, G. (2011, December). In 2011 IEEE 11th international conference on data mining (pp. 497-506). IEEE.

Fism: factored item similarity models for top-n recommender systems.
Kabbur, S., Ning, X., & Karypis, G. (2013, August). In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 659-667). 

NAIS: Neural attentive item similarity model for recommendation.
He, X., He, Z., Song, J., Liu, Z., Jiang, Y. G., & Chua, T. S. (2018). IEEE Transactions on Knowledge and Data Engineering, 30(12), 2354-2366. 

Autorec: Autoencoders meet collaborative filtering.
Sedhain, S., Menon, A. K., Sanner, S., & Xie, L. (2015, May). In Proceedings of the 24th international conference on World Wide Web (pp. 111-112).

Collaborative denoising auto-encoders for top-n recommender systems.
Wu, Y., DuBois, C., Zheng, A. X., & Ester, M. (2016, February). In Proceedings of the ninth ACM international conference on web search and data mining (pp. 153-162).

Variational autoencoders for collaborative filtering.
Liang, D., Krishnan, R. G., Hoffman, M. D., & Jebara, T. (2018, April). In Proceedings of the 2018 world wide web conference (pp. 689-698).

Recommending based on implicit feedback.
Jannach, D., Lerche, L., & Zanker, M. (2018). In Social information access: systems and technologies (pp. 510-569). Cham: Springer International Publishing. 

Modeling User Exposure in Recommendation.
Liang, D., Charlin, L., McInerney, J., & Blei, D. M. (2016, April). Modeling user exposure in recommendation. In Proceedings of the 25th international conference on World Wide Web (pp. 951-961). 

BPR: Bayesian personalized ranking from implicit feedback.
Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012). arXiv preprint arXiv:1205.2618. 

Climf: learning to maximize reciprocal rank with collaborative less-is-more filtering.
Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Oliver, N., & Hanjalic, A. (2012, September). In Proceedings of the sixth ACM conference on Recommender systems (pp. 139-146).