Current - Issue

1. Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender Systems Handbook
(pp. 1–35). Springer. https://doi.org/10.1007/978-0-387- 85820-3_1
2. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language
understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational
Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186. https://doi.org/10.18653/v1/N19-
14233. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask
learners. OpenAI Blog,1(8),9.https://cdn.openai.com/better-
languagemodels/language_models_are_ unsupervised_multitask_learners.pdf
4. Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., ... & Amodei, D. (2020). Scaling laws for neural
language models. arXiv preprint arXiv:2001.08361.
5. Statista (2023). Conversational Commerce Market - Growth, Trends, The size of the chatbot market is forecast to reach around 1.25
billion U.S. dollars in 2025, a great increase from the market size in 2016, which stood at 190.8 million U.S. dollars. Global chatbot
market 2016-2025 | Statista
6. Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management,
24(5), 513-523. https://doi.org/10.1016/0306-4573(88)90021-0
7. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You
Need. Advances in Neural Information Processing Systems, 30, 5998–6008.
8. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., & Amodei, D. (2020). Language Models are Few-Shot
Learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
9. Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2009). Bayesian Personalized Ranking from Implicit Feedback.
Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, 452–461.
10. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017). Neural Collaborative Filtering. Proceedings of the 26th International
Conference on World Wide Web, 173–182.
11. Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). Fine-Tuning BERT for Text Classification Tasks. China National Conference on
Chinese Computational Linguistics, 194–206.
12. Zhang, Y., & Chen, X. (2020). Explainable Recommendation: A Survey and New Perspectives. Foundations and Trends in
Information Retrieval, 14(1), 1–101.
13. Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., & Zettlemoyer, L. (2020). BART: Denoising Sequence-
to-Sequence Pre-training for Natural Language Generation. Proceedings of the Association for Computational Linguistics, 7871–7880.
14. Wang, H., Zhang, F., Hou, M., Xie, X., Guo, M., & Liu, Q. (2018). SHINE: Signed Heterogeneous Information Network Embedding
for Sentiment Link Prediction. Proceedings of the ACM International Conference on Web Search and Data Mining, 592–600.
15. Chen, T., Yin, H., Nguyen, Q. V. H., Peng, W. C., Li, X., & Zhou, X. (2021). Attentional Intent-Aware Recommendation System.
IEEE Transactions on Knowledge and Data Engineering, 33(4), 1460–1474.