Abstract
This paper proposes the idea of movie recommenders that draw from the users’ everyday life happenings as documented through their personal social media posts to produce relevant recommendations. We conducted an experimental study to understand the important dimensions to consider in the design of such a recommendation system. We began with the design hypothesis that matching keywords and categories from users’ social media posts to those from movie plots may increase users’ perceived relevance of movie recommendations. Our analysis revealed that beyond keywords and categories, emotional context and genre of movies are important aspects to consider. Based on these findings, we discuss the implications on the design of movie recommendation systems leveraging users’ everyday life through social media posts.
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References
Hektner, J.M., Schmidt, J.A., Csikszentmihalyi, M.: Experience sampling method: measuring the quality of everyday life. Sage (2007)
Rimé, B., Paez, D., Kanyangara, P., Yzerbyt, V.: The social sharing of emotions in interpersonal and in collective situations: Common psychosocial consequences. In Emotion regulation and well-being, pp. 147–163. Springer, New York (2011). https://doi.org/10.1007/978-1-4419-6953-8_9
Veer, E., Ozanne, L.K., Michael Hall, C.: Sharing cathartic stories online: the internet as a means of expression following a crisis event. J. Cons. Behav. 15(4), 314–324 (2016)
Bazarova, N.N., Choi, Y.H., Sosik, V.S., Cosley, D., Whitlock, J.: Social sharing of emotions on Facebook: channel differences, satisfaction, and replies. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 154–164 (2015)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005)
Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_3
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)
Cantador, I., Fernández-Tobías, I., Berkovsky, S., Cremonesi, P.: Cross-domain recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 919–959. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_27
Pham, X.H., Jung, J.J., Vu, L.A., Park, S.-B.: Exploiting social contexts for movie recommendation. Malays. J. Comput. Sci. 27(1), 68–79 (2014)
Das, D., Chidananda, H.T., Sahoo, L.: Personalized movie recommendation system using twitter data, pp. 339–347 (2018)
Dooms, S., Martens, L.: Harvesting movie ratings from structured data in social media by Simon Dooms and Luc Martens with Ching-man Au Yeung as coordinator. ACM Sigweb Newsl. 2014, 4 (2014)
Schedl, M.: Ameliorating music recommendation: integrating music content, music context, and user context for improved music retrieval and recommendation. MoMM 2013, pp. 3–9, December (2013)
Wei, S., Zheng, X., Chen, D., Chen, C.: A hybrid approach for movie recommendation via tags and ratings. Electron. Commer. Res. Appl. 18, 83–94 (2016)
Evgenia Wasserman Pritsker, Tsvi Kuflik, and Einat Minkov. Assessing the Contribution of Twitter’s Textual Information to Graph-based Recommendation. pages 511–516, March 2017
Leung, J.K., Griva, I., Kennedy, W.G.: Making use of affective features from media content metadata for better movie recommendation making. ArXiv (2020)
Chien, C.-Y., Qiu, G.-H., Lu, W.-H.: A movie trailer recommendation system based on pre-trained vector of relationship and scenario content discovered from plot summaries and social media. In: 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), pp. 8959918, November (2019)
Chu, S.L., Brown, S., Park, H., Brittnie Spornhauer. Towards personalized movie selection for wellness: investigating event-inspired movies. Int. J. Hum-Comput. Interact. 36(16), 1514–1526 (2020)
Zhao, D., Du, N., Chang, Z., Li, Y.: Keyword extraction for social media short text. In: 2017 14th Web Information Systems and Applications Conference (WISA), pp. 251–256 (2017)
GroupLens Research. Movielens. https://movielens.org/
IBM Natural Language Processing. https://www.ibm.com/cloud/watson-natural-language-understanding
Acknowledgements
This project was partially supported by NSF grant #1942937, CAREER: Bridging Formal and Everyday Learning through Wearable Technologies: Towards a Connected Learning Paradigm.
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Kulkarni, A., Powell, L., Murphy, S., Rao, N., Chu, S.L. (2023). Everyday-Inspired Movies: Towards the Design of Movie Recommender Systems based on Everyday Life through Personal Social Media. In: Abdelnour Nocera, J., Kristín Lárusdóttir, M., Petrie, H., Piccinno, A., Winckler, M. (eds) Human-Computer Interaction – INTERACT 2023. INTERACT 2023. Lecture Notes in Computer Science, vol 14144. Springer, Cham. https://doi.org/10.1007/978-3-031-42286-7_9
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