과제정보
본 연구는 과학기술정보통신부 및 정보통신기획평가원의 ICT혁신인재4.0 사업의 연구결과로 수행되었음" (IITP-2024-RS-2022-00156299)
참고문헌
- A. Beutel, P. Covington, S. Jain, C. Xu, J. Li, V. Gatto and E.H. Chi, "Latent Cross: Making Use of Context in Recurrent Recommender-Systems," Proceedings of in ACM Conference on Web Search and Data Mining (WSDM), pp. 46-54, Feb. 5-9, Marina Del Rey, CA, USA, 2018.
- H. Steck and D. Liang, "Negative Interactions for Improved Collaborative Filtering: Don't Go Deeper, Go Higher," Proceedings of the 15th ACM Conference on Recommender Systems (RecSys), pp. 34-43, Sep. 27-Oct. 1, Amsterdam, Netherlands, 2021.
- F. Sun, J. Liu, J. Wu, C. Pei, X. Lin, W. Ou and P. Jiang. "BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer," Proceedings of in the 28th ACM International Conference on Information and Knowledge Management (CIKM), pp. 1441-1450, Nov. 3-7, Beijing, China, 2019.
- H. Steck, "Embarrassingly Shallow Autoencoders for Sparse Data," Proceedings of the World Wide Web Conference (WWW), pp. 3251-3257, May. 13-17, CA, USA, 2019.
- Kaggle의 MovieLens 데이터셋, https://www.kaggle.com/datasets/grouplens/movielens-20m-dataset?select=genome_tags.csv
- J. Devlin, M.W. Chang, K. Lee and K. Toutanova, "Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding," Proceedings of ArXiv Preprint, 2018, arXiv:1810.04805.