找不到想看的電視節目是個很困擾的問題。隨著不同的時段，節目內容也隨之改變，電視節目不像YouTube或是其他影音平台可以隨時想看就看。不同於其他電視節目推薦方法僅使用使用者觀看的時間或是節目的標籤（tag）來推薦節目，本篇研究同時考慮使用者的觀看時間以及節目的標籤，，並提出一個結合長期及短期觀看行為的方法使電視節目推薦準確度提高。

Unable to find a preferred TV show to watch while watching TV is an annoying problem. Unlike YouTube or other video platforms which can choose and play videos anytime, available shows on TV are changing while time is going. Users are able to save plenty of time on searching shows if the desired ones can be predicted in advance. Unlike other existing works, which use either watching time or tags of shows, our work uses both of them, and a method combining long-term and short-term watching behavior is proposed to improve the accuracy of predicting TV shows.

[1] Cho, K. J., Lee, Y. C., Han, K., Choi, J., & Kim, S. W. (2019, April). No, that's not my feedback: TV show recommendation using watchable interval. In 2019 IEEE 35th International Conference on Data Engineering (ICDE) (pp. 316-327). IEEE.
[2] Yin, F., Li, S., Ji, M., & Wang, Y. (2022). Neural TV program recommendation with label and user dual attention. Applied Intelligence, 52(1), 19-32.
[3] Li, G., Qiu, L., Yu, C., Cao, H., Liu, Y., & Yang, C. (2020). IPTV channel zapping recommendation with attention mechanism. IEEE Transactions on Multimedia, 23, 538-549.
[4] Oh, J., Kim, S., Kim, J., & Yu, H. (2014). When to recommend: A new issue on TV show recommendation. Information Sciences, 280, 261-274.
[5] N. Srebro and T. Jaakkola, “Weighted low-rank approximations,” in Proc. of the 20th Int’l Conf. on Machine Learning (ICML), pp. 720–727, 2003.
[6] Sharma, B., Hashmi, A., Gupta, C., Khalaf, O. I., Abdulsahib, G. M., & Itani, M. M. (2022). Hybrid Sparrow Clustered (HSC) Algorithm for Top-N Recommendation System. Symmetry, 14(4), 793.
[7] Chae, D. K., Kim, J., Chau, D. H., & Kim, S. W. (2020, July). Ar-cf: Augmenting virtual users and items in collaborative filtering for addressing cold-start problems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1251-1260).
[8] ZHU, Xiaosong, et al. Facing Cold-Start: A live TV recommender system based on neural networks. IEEE Access, 2020, 8: 131286-131298.
[9] Xu, J. A., & Araki, K. (2006, January). A SVM-based personal recommendation system for TV programs. In 2006 12th International Multi-Media Modelling Conference (pp. 4-pp). IEEE.
[10] He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017, April). Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web (pp. 173-182).