Abstract
Recommender systems have become essential in several domains to deal with the problem of information overload. Collaborative filtering is one of the most popularly used paradigm of recommender systems for over a decade. The personalized recommender systems use past preference history of the users to make future recommendations for them. The cold start problem of recommender system concerns with the personalized recommendation to the users having no or few past history. In this work we propose an approach to learn implicit user preferences by making use of YouTube Video Tags. The profile of a new user is created from his/her preferences in watching the YouTube videos. This profile is generic and may be used for a wide variety of domains of recommender systems. In this work we have used it for a biography recommender system. However this may be used for several other types of recommender system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Adomavicius, G., Mobasher, B., Ricci, F., Tuzhilin, A.: Context-aware recommender systems. AI Magaz. 32(3), 67–80 (2011)
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. (IEEE) 17(6), 734–749 (2005)
Berjani, B., Strufe, T.: A recommendation system for spots in location-based online social networks. In: Proceedings of the 4th Workshop on Social Network Systems, vol. 4. ACM, Salzburg, Austria (2011)
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adapt. Interact. 12(4), 331–370 (2002)
Gemmell, J., Shepitsen, A., Mobasher, B., Burke, R.: Personalization in folksonomies based on tag clustering. Intell. Tech. Web Personalization Recommender Syst. 12 (2008)
JSON Objects. https://www.w3schools.com/js/js_json_objects.asp
Li, X., Guo, L., Zhao, Y.E.: Tag-based social interest discovery. In: Proceedings of the 17th International Conference on World Wide Web, pp. 675–684. ACM, April 2008
Liu, B., Fu, Y., Yao, Z., Xiong, H.: Learning geographical preferences for point-of-interest recommendation. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1043–1051. ACM (2013)
Mokbel, M., Bao, J., Eldawy, A., Levandoski, J., Sarwat, M.: Personalization, socialization, and recommendations in location-based services 2.0. In: 5th International VLDB Workshop on Personalized Access, Profile Management and Context Awareness in Databases (PersDB). VLDB. ACM, Seattle (2011)
Safoury, L., Salah, A.: Exploiting user demographic attributes for solving cold-start problem in recommender system. Lect. Notes Softw. Eng. 1(3), 303 (2013)
Tiwari, S., Kaushik, S.: Modeling personalized recommendations of unvisited tourist places using genetic algorithms. In: Chu, W., Kikuchi, S., Bhalla, S. (eds.) DNIS 2015. LNCS, vol. 8999, pp. 264–276. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16313-0_20
Tiwari, S., Kaushik, S.: Evolving recommendations from past travel sequences using soft computing techniques. Int. J. Comput. Sci. Eng. 14(3), 242–254 (2017)
Tiwari, S., Kaushik, S., Jagwani, P.: Location based recommender systems: Architecture, trends and research areas (2012)
Tiwari, S., Kaushik, S.: A non functional properties based web service recommender system. In: 2010 International Conference on Computational Intelligence and Software Engineering (CiSE), pp. 1–4. IEEE, December 2010
Zenebe, A., Zhou, L., Norcio, A.F.: User preferences discovery using fuzzy models. Fuzzy Sets Syst. 161(23), 3044–3063 (2010)
Youtube Data API. https://developers.google.com/youtube/v3/
https://www.mushroomnetworks.com/infographics/youtube---the-2nd-largest-search-engine-infographic/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Tiwari, S., Jain, A., Kothari, P., Upadhyay, R., Singh, K. (2018). Learning User Preferences for Recommender System Using YouTube Videos Tags. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10963. Springer, Cham. https://doi.org/10.1007/978-3-319-95171-3_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-95171-3_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95170-6
Online ISBN: 978-3-319-95171-3
eBook Packages: Computer ScienceComputer Science (R0)