Fetching the paper…
Reading the bibliography…
Despite recommender systems play a key role in network content platforms, mining the user's interests is still a significant challenge.
Carpenter, G.A., Grossberg, S.: The art of adaptive pattern recognition by a self-organizing neural network. Computer 21
1988
Earlier work this paper cites.
Seiffert, U.: Self-organizing neural networks: Recent advances and applications (2001)
2001
Earlier work this paper cites.
Kohonen, T., Honkela, T.: Kohonen network. Scholarpedia 2
2007
Earlier work this paper cites.
Bobadilla, J., Ortega, F., Hernando, A., Bernal, J.: A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-based systems 26
2012
Earlier work this paper cites.
Meyffret, S., Guillot, E., Médini, L., Laforest, F.: RED: a rich epinions dataset for recommender systems. Ph.D. thesis, LIRIS (2012)
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
Ben-Shimon, D., Tsikinovsky, A., Friedmann, M., Shapira, B., Rokach, L., Hoerle, J.: Recsys challenge 2015 and the yoochoose dataset. In: Proceedings of the 9th ACM Conference on Recommender Systems. pp. 357–358 (2015)
2015
Earlier work this paper cites.
Harper, F.M., Konstan, J.A.: The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5
2015
Earlier work this paper cites.
Cheng, H.T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., Ispir, M., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems. pp. 7–10 (2016)
2016
Earlier work this paper cites.
Covington, P., Adams, J., Sargin, E.: Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems. pp. 191–198 (2016)
2016
Earlier work this paper cites.
Wan, M., McAuley, J.: Modeling ambiguity, subjectivity, and diverging viewpoints in opinion question answering systems. In: 2016 IEEE 16th international conference on data mining (ICDM). pp. 489–498. IEEE (2016)
2016
Earlier work this paper cites.
Gope, J., Jain, S.K.: A survey on solving cold start problem in recommender systems. In: 2017 International Conference on Computing, Communication and Automation (ICCCA). pp. 133–138. IEEE (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Li, D., Luo, Z., Ding, Y., Tang, J., Guo-Zheng Sun, G., Dai, X., Du, J., Zhang, J., Kong, S.: User-level microblogging recommendation incorporating social influence. Journal of the Association for Information Science and Technology 68
2017
Earlier work this paper cites.
2018
Cited alongside, same era.
Lian, J., Zhou, X., Zhang, F., Chen, Z., Xie, X., Sun, G.: xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. pp. 1754–1763 (2018)
2018
Cited alongside, same era.
Zhou, G., Zhu, X., Song, C., Fan, Y., Zhu, H., Ma, X., Yan, Y., Jin, J., Li, H., Gai, K.: Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 1059–1068 (2018)
2018
Cited alongside, same era.
Ni, J., Li, J., McAuley, J.: Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). pp. 188–197 (2019)
Su, Y., Han, X., Zhang, Z., Lin, Y., Li, P., Liu, Z., Zhou, J., Sun, M.: Cokebert: Contextual knowledge selection and embedding towards enhanced pre-trained language models. AI Open 2
2021
Later among the works it cites.
Xia, L., Xu, Y., Huang, C., Dai, P., Bo, L.: Graph meta network for multi-behavior recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 757–766 (2021)
2021
Later among the works it cites.
2022
Later among the works it cites.
Dong, Q., Niu, S., Yuan, T., Li, Y.: Disentangled graph recurrent network for document ranking. Data Science and Engineering 7
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Zhang, C., Wang, H., Yang, S., Gao, Y.: A contextual bandit approach to personalized online recommendation via sparse interactions. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. pp. 394–406. Springer (2019)
2019
Cited alongside, same era.
Zhou, G., Mou, N., Fan, Y., Pi, Q., Bian, W., Zhou, C., Zhu, X., Gai, K.: Deep interest evolution network for click-through rate prediction. In: Proceedings of the AAAI conference on artificial intelligence. vol. 33, pp. 5941–5948 (2019)
2019
Cited alongside, same era.
Lu, Y., Fang, Y., Shi, C.: Meta-learning on heterogeneous information networks for cold-start recommendation. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 1563–1573 (2020)
2020
Cited alongside, same era.
Qin, J., Zhang, W., Wu, X., Jin, J., Fang, Y., Yu, Y.: User behavior retrieval for click-through rate prediction. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 2347–2356 (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Dong, Q., Niu, S.: Latent graph recurrent network for document ranking. In: Database Systems for Advanced Applications: 26th International Conference, DASFAA 2021, Taipei, Taiwan, April 11–14, 2021, Proceedings, Part II 26. pp. 88–103. Springer (2021)
2021
Cited alongside, same era.
Dong, Q., Niu, S.: Legal judgment prediction via relational learning. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 983–992 (2021)
2021
Cited alongside, same era.
Xia, L., Huang, C., Xu, Y., Pei, J.: Multi-behavior sequential recommendation with temporal graph transformer. IEEE Transactions on Knowledge and Data Engineering (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.