OrangeMusic : An orange computing‐inspired recommender framework in internet of music things
Samarjit Roy, Anwesha Mukherjee, Debashis De · Internet Technology Letters · 2021
Abstract Recent computational analytics in the domain of the Internet of Things provides crowd‐sourced reviews for decision assistance for innumerable aspects of our living standards and socio‐entertainments. However, one of the most significant tasks for obtainable online music libraries and websites is the demand for personalized and professionalized courses of action for music listeners and composers to elect suitable musical performances. In this paper, we illustrate a hybrid matrix factorization‐based content‐sensitive music recommender schema on the Internet of Music Things. Emerging orange computing technology offers a harmonic fusion framework for psychological care and happiness‐concerned computing. We elucidate the projected music recommender paradigm in the domain of Internet of Music Things, titled as OrangeMusic. The OrangeMusic schema differs from the earlier contributions in the following aspects: (a) Orange computing‐based information fusion framework is applied on the Internet of Music Things; (b) Provided musical content revisions can be exposed by listeners' rating metrics and be exploited to amend original listener‐provided ratings; (c) Music listeners' preferences and musical items are incorporated into the standard matrix factorization mechanism. The performance metrics flourish that our proposed OrangeMusic presents a proficient rating prediction and intensifies the accuracy of content‐sensitive music recommendation expressively.