Reference: | Allesiardo, R., Féraud, R., & Bouneffouf, D. (2014). A neural networks committee for the contextual bandit problem. In Processings of the international conference on neural information processing (Vol. 8834, pp. 374–381). doi: 10.1007/978-3-319 -12637-1_47 Auer, P. (2002). Using confidence bounds for exploitation-exploration trade-offs. Ma- chine Learning Research, 3(Nov), 397–422. Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-time analysis of the mul- tiarmed bandit problem. Machine learning, 47(2), 235–256. doi: 10.1023/A: 1013689704352 Auer, P., Cesa-Bianchi, N., Freund, Y., & Schapire, R. E. (2002). The nonstochastic multiarmed bandit problem. SIAM journal on computing, 32(1), 48–77. doi: 10 .1137/S0097539701398375 Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015). Weight uncertainty in neural networks. In Proceedings of the 32nd international conference on inter- national conference on machine learning (Vol. 37, pp. 1613–1622). Bouneffouf, D., Bouzeghoub, A., & Gançarski, A. L. (2012). A contextual-bandit algo- rithm for mobile context-aware recommender system. In International conference on neural information processing (pp. 324–331). Burtini, G., Loeppky, J., & Lawrence, R. (2015). A survey of online experiment design with the stochastic multi-armed bandit. Retrieved from https://arxiv.org/abs/1510.00757 Cai, J., Wohn, D. Y., Mittal, A., & Sureshbabu, D. (2018). Utilitarian and hedonic moti- vations for live streaming shopping. In Proceedings of the 2018 acm international conference on interactive experiences for tv and online video (p. 81–88). doi: 10.1145/3210825.3210837 Cheng, Z., & Shen, J. (2016, April). On effective location-aware music recommen- dation. ACM Transactions on Information Systems (TOIS), 34(2), 1–32. doi: 10.1145/2846092 Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using rnn encoder-decoder for statistical machine translation. Retrieved from https://arxiv.org/abs/1406.1078 Choe, D.-E., Kim, H.-C., & Kim, M.-H. (2021). Sequence-based modeling of deep learn- ing with lstm and gru networks for structural damage detection of floating offshore wind turbine blades. Renewable Energy, 174, 218–235. Chu, W., Li, L., Reyzin, L., & Schapire, R. (2011). Contextual bandits with linear payoff functions. In Proceedings of the 14th international conference on artificial intelli- gence and statistics (pp. 208–214). Docherty, I. (2018). New governance challenges in the era of ‘smart’mobility. In Governance of the smart mobility transition. Du, C., Gao, Z., Yuan, S., Gao, L., Li, Z., Zeng, Y., ... Lee, K.-C. (2021). Exploration in online advertising systems with deep uncertainty-aware learning. In Proceedings of the 27th acm sigkdd conference on knowledge discovery & data mining (pp. 2792– 2801). Fang, H., Zhang, D., Shu, Y., & Guo, G. (2020). Deep learning for sequential recom- mendation: Algorithms, influential factors, and evaluations. ACM Transactions on Information Systems (TOIS), 39(1), 1–42. Gal, Y., & Ghahramani, Z. (2016). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning (pp. 1050–1059). Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., ... others (2021). A survey of uncertainty in deep neural networks. Retrieved from https://arxiv.org/abs/2107.03342 Gediminas, A., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6), 734-749. doi: https://doi.org/10.1109/ TKDE.2005.99 Gulrajani, I., Kumar, K., Ahmed, F., Taiga, A. A., Visin, F., Vazquez, D., & Courville, A. (2016). Pixelvae: A latent variable model for natural images. Retrieved from https://arxiv.org/abs/1611.05013 He, X., Chen, T., Kan, M.-Y., & Chen, X. (2015). Trirank: Review-aware explainable recommendation by modeling aspects. In Proceedings of the 24th acm international on conference on information and knowledge management (p. 1661–1670). doi: 10.1145/2806416.2806504 Hu, M., & Chaudhry, S. S. (2020). Enhancing consumer engagement in e-commerce live streaming via relational bonds. Internet Research, 30(3). doi: 10.1108/INTR-03 -2019-0082 Kakade, S. M., Shalev-Shwartz, S., & Tewari, A. (2008). Efficient bandit algorithms for online multiclass prediction. In Proceedings of the 25th international conference on machine learning (pp. 440–447). doi: 10.1145/1390156.1390212 Katehakis, M. N., & Veinott Jr, A. F. (1987). The multi-armed bandit problem: Decom- position and computation. Mathematics of Operations Research, 12(2), 262–268. doi: 10.1287/moor.12.2.262 Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. Retrieved from https://arxiv.org/abs/1312.6114 Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46(5), 604–632. Ko, H.-C., & Chen, Z.-Y. (2020). Exploring the factors driving live streaming shopping intention: a perspective of parasocial interaction. In Proceedings of the 2020 inter- national conference on management of e-commerce and e-government (pp. 36–40). Langford, J., & Zhang, T. (2007). The Epoch-Greedy algorithm for contextual multi- armed bandits. In Proceedings of the 20th international conference on neural in- formation processing systems (p. 817–824). Lauret, P., Fock, E., Randrianarivony, R. N., & Manicom-Ramsamy, J.-F. (2008). Bayesian neural network approach to short time load forecasting. Energy conver- sion and management, 49(5), 1156–1166. Lee, H. I., Choi, I. Y., Moon, H. S., & Kim, J. K. (2020). A multi-period product recom- mender system in online food market based on recurrent neural networks. Sustain- ability, 12(3), 969. Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., & Ma, J. (2017). Neural attentive session-based recommendation. In (pp. 1419–1428). doi: 10.1145/3132847 Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010). A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on world wide web (pp. 661–670). doi: 10.1145/1772690.1772758 Li, S., Karatzoglou, A., & Gentile, C. (2016). Collaborative filtering bandits. In Proceed- ings of the 39th international acm sigir conference on research and development in information retrieval (pp. 539–548). Lin, C.-Y., & Chen, H.-S. (2019). Personalized channel recommendation on live streaming platforms. Multimedia Tools and Applications, 78(2), 1999–2015. Liu, Y. W., Lin, C. Y., & Huang, J. L. (2015). Live streaming channel recommendation using hits algorithm. In 2015 ieee international conference on consumer electronics taiwan (pp. 118–119). Martinez-Cantin, R., De Freitas, N., Brochu, E., Castellanos, J., & Doucet, A. (2009). A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot. Autonomous Robots, 27(2), 93–103. doi: 10.1007/s10514-009-9130-2 Mullachery, V., Khera, A., & Husain, A. (2018). Bayesian neural networks. Retrieved from https://arxiv.org/abs/1801.07710 Pradel, B., Sean, S., Delporte, J., Guérif, S., Rouveirol, C., Usunier, N., ... France, O. (2011). A case study in a recommender system based on purchase data. In Proceed- ings of the 17th acm sigkdd international conference on knowledge discovery and data mining - kdd ’11 (pp. 377–385). doi: 10.1145/2020408 Rappaz, J., McAuley, J., & Aberer, K. (2021). Recommendation on live-streaming plat- forms: Dynamic availability and repeat consumption. In Fifteenth acm conference on recommender systems (pp. 390–399). Reinartz, W. J., & Kumar, V. (2003). The impact of customer relationship characteristics on profitable lifetime duration. Journal of marketing, 67(1), 77–99. Santana, L. L. B. d. S., Souza, A. B. S., Santana, D. L., Dourado, W. A., & Durão, F. A. (2017). Evaluating ensemble strategies for recommender systems under metadata reduction. In Proceedings of the 23rd brazillian symposium on multimedia and the web (pp. 125–132). doi: 10.1145/3126858.3126879 Satyal, S., Weber, I., Paik, H.-y., Di Ciccio, C., & Mendling, J. (2018). AB testing for process versions with contextual multi-armed bandit algorithms. In Proceedings of the international conference on advanced information systems engineering (pp. 19–34). doi: 10.1007/978-3-319-91563-0_2 Shahrampour, S., Rakhlin, A., & Jadbabaie, A. (2017). Multi-armed bandits in multi-agent networks. In Proceedings of the 2017 ieee international conference on acous- tics, speech and signal processing (p. 2786-2790). doi: 10.1109/ICASSP.2017.7952664 Shani, G., & Gunawardana, A. (2011). Evaluating recommendation systems. In F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor (Eds.), Recommender systems handbook (pp. 257–297). doi: 10.1007/978-0-387-85820-3_8 Su, X. (2019, dec). An empirical study on the influencing factors of e-commerce live streaming. In 2019 international conference on economic management and model engineering, icemme 2019 (pp. 492–496). doi: 10.1109/ICEMME49371 .2019.00103 Sun, Y., Shao, X., Li, X., Guo, Y., & Nie, K. (2019). How live streaming influences purchase intentions in social commerce: An it affordance perspective. Electronic Commerce Research and Applications, 37, 100886. doi: https://doi.org/10.1016/ j.elerap.2019.100886 Takahashi, R., & Zhang, S. (2017). Towards bursting filter bubble via contextual risks and uncertainties. Retrieved from https://arxiv.org/abs/1706.09985 Truong, Q.-T., Salah, A., & Lauw, H. W. (2021). Bilateral variational autoencoder for collaborative filtering. In Proceedings of the 14th acm international conference on web search and data mining (pp. 292–300). Vanchinathan, H. P., Nikolic, I., De Bona, F., & Krause, A. (2014). Explore-exploit in top-n recommender systems via gaussian processes. In Proceedings of the 8th acm conference on recommender systems (pp. 225–232). Vuyyuru, V. A., Rao, G. A., & Murthy, Y. (2021). A novel weather prediction model using a hybrid mechanism based on mlp and vae with fire-fly optimization algorithm. Evolutionary Intelligence, 14(2), 1173–1185. Wang, H., Wu, Q., & Wang, H. (2016). Learning hidden features for contextual bandits. In Proceedings of the 25th acm international on conference on information and knowledge management (pp. 1633–1642). Wang, Z., Lee, S.-J., & Lee, K.-R. (2018). Factors influencing product purchase intentionin taobao live streaming shopping. Journal of Digital Contents Society, 19(4), 649–659. Wikipedia. (2022). Livestream shopping — Wikipedia, the free encyclopedia. Retrieved from http://en.wikipedia.org/w/index.php?title=Livestream\\ %20shopping&oldid=1065424656 Wongkitrungrueng, A., & Assarut, N. (2020). The role of live streaming in building consumer trust and engagement with social commerce sellers. Journal of Business Research, 117, 543-556. doi: https://doi.org/10.1016/j.jbusres.2018.08.032 Wongkitrungrueng, A., Dehouche, N., & Assarut, N. (2020). Live streaming commerce from the sellers’perspective: implications for online relationship marketing. Jour- nal of Marketing Management, 36(5-6), 488–518. Xu, X., Wu, J.-H., & Li, Q. (2020). What drives consumer shopping behavior in live streaming commerce? Journal of Electronic Commerce Research, 21(3), 144–167. Xue, F., He, X., Wang, X., Xu, J., Liu, K., & Hong, R. (2019, April). Deep item-based col- laborative filtering for top-N recommendation. ACM Transactions on Information Systems (TOIS), 37(3). doi: 10.1145/3314578 Yang, T.-W., Shih, W.-Y., Huang, J.-L., Ting, W.-C., & Liu, P.-C. (2013). A hybrid preference-aware recommendation algorithm for live streaming channels. In 2013 conference on technologies and applications of artificial intelligence (pp. 188– 193). Zhang, S., Liu, H., He, J., Han, S., & Du, X. (2021). Deep sequential model for anchor recommendation on live streaming platforms. Big Data Mining and Analytics, 4(3), 173–182. Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR), 52(1), 1–38. doi: 10.1145/3285029 Zhang, X., Xie, H., Li, H., & CS Lui, J. (2020). Conversational contextual bandit: Algorithm and application. In Proceedings of the web conference 2020 (pp. 662–672). Zhou, D., Li, L., & Gu, Q. (2020). Neural contextual bandits with UCB-based exploration. In Proceedings of the 37th international conference on machine learning (Vol. 119, pp. 11492–11502). Zhou, M., Huang, J., Wu, K., Huang, X., Kong, N., & Campy, K. S. (2021, nov). Characterizing Chinese consumers’ intention to use live e-commerce shopping. Technology in Society, 67, 101767. doi: 10.1016/J.TECHSOC.2021.101767 Zou, L., Xia, L., Ding, Z., Song, J., Liu, W., & Yin, D. (2019). Reinforcement learning to optimize long-term user engagement in recommender systems. In Proceedings of the 25th acm sigkdd international conference on knowledge discovery & data mining (pp. 2810–2818). |