Abstract
Events have become a common way for activity organization in many digital platforms. Event participant prediction is an important problem when planning future events for these platforms. Previous works have found that cold-start recommendation techniques can be used to solve the problem effectively. However, for many starting platforms, training data they own is limited, and may not be sufficient to learn accurate recommendation models. On the other hand, social media retweeting is a kind of event participant data that can be obtained easily. In this paper, we propose to utilize social media retweeting to help improve event participant prediction models. Our approach uses an entity-connect knowledge graph to bridge the social media and the target domain, assuming that event descriptions in the target domain are written in the same language as social media tweets. Experimental evaluation with real-world event participation datasets shows that adding social media retweeting data with our approach does steadily improve prediction accuracy in the target domain.
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References
Asur, S., Huberman, B.A.: Predicting the future with social media. In: Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 492–499. IEEE (2010)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, vol. 26 (2013)
Du, R., Yu, Z., Mei, T., Wang, Z., Wang, Z., Guo, B.: Predicting activity attendance in event-based social networks: content, context and social influence. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 425–434 (2014)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182. ACM, Perth, Australia (2017)
Kim, J.: Events as property exemplifications. In: Brand, M., Walton, D. (eds.) Action Theory. SYLI, pp. 159–177. Springer, Dordrecht (1976). https://doi.org/10.1007/978-94-010-9074-2_9
Li, K., Lu, W., Bhagat, S., Lakshmanan, L.V., Yu, C.: On social event organization. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1206–1215 (2014)
Liu, X., He, Q., Tian, Y., Lee, W.C., McPherson, J., Han, J.: Event-based social networks: linking the online and offline social worlds. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1032–1040 (2012)
Man, T., Shen, H., Jin, X., Cheng, X.: Cross-domain recommendation: an embedding and mapping approach. In: IJCAI, vol. 17, pp. 2464–2470 (2017)
Pai, P.F., Liu, C.H.: Predicting vehicle sales by sentiment analysis of Twitter data and stock market values. IEEE Access 6, 57655–57662 (2018)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)
Qiao, Z., Zhang, P., Zhou, C., Cao, Y., Guo, L., Zhang, Y.: Event recommendation in event-based social networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)
Wang, H., et al.: A DNN-based cross-domain recommender system for alleviating cold-start problem in e-commerce. IEEE Open J. Ind. Electron. Soc. 1, 194–206 (2020)
Wei, W., Mao, Y., Wang, B.: Twitter volume spikes and stock options pricing. Comput. Commun. 73, 271–281 (2016)
Yu, Z., et al.: Who should I invite for my party? Combining user preference and influence maximization for social events. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 879–883 (2015)
Zhang, X., Zhao, J., Cao, G.: Who will attend?-Predicting event attendance in event-based social network. In: 2015 16th IEEE International Conference on Mobile Data Management, vol. 1, pp. 74–83. IEEE (2015)
Zhu, Y., et al.: Addressing the item cold-start problem by attribute-driven active learning. IEEE Trans. Knowl. Data Eng. 32(4), 631–644 (2019)
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This research is partially supported by JST CREST Grant Number JPMJCR21F2.
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Zhang, Y., Hara, T. (2022). Utilizing Social Media Retweeting for Improving Event Participant Prediction. In: Chbeir, R., Huang, H., Silvestri, F., Manolopoulos, Y., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2022. WISE 2022. Lecture Notes in Computer Science, vol 13724. Springer, Cham. https://doi.org/10.1007/978-3-031-20891-1_1
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