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.
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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
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DOI: https://doi.org/10.1007/978-3-319-95171-3_36
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