Abstract
Unlike regular DVD stores that allow the customer to choose from a relatively small number of products, online music platforms such as Spotify or YouTube offer large numbers of songs to their users, making the online selection process quite different from the conventional one. The goal of any recommendation system is to solve this issue by making suggestions that fit the user’s preferences. The InVibe project offers a free web platform for music listening that uses its custom recommendation system to help users explore the amount of music in a natural and exciting manner. The paper will focus on the collaborative filtering algorithms used to build the recommender system, the implementation of the web application and the overall architecture designed to integrate the recommender module with the web platform.
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The authors would like to thank the anonymous reviewers for their constructive comments and feedback on the manuscript.
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Paraicu, I., Dobre, C. (2017). Online Music Application with Recommendation System. In: Mavromoustakis, C., Mastorakis, G., Dobre, C. (eds) Advances in Mobile Cloud Computing and Big Data in the 5G Era. Studies in Big Data, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-45145-9_15
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DOI: https://doi.org/10.1007/978-3-319-45145-9_15
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