Paper Title : MUSIC RECOMMENDATON BASED ON MUSIC CHARACTERISTICS AND USER INTRINSIC COMPONENTS
ISSN : 2394-2231
Year of Publication : 2021
MLA Style: C.Mani MCA., M.E., V.Gopalakrishnan "MUSIC RECOMMENDATON BASED ON MUSIC CHARACTERISTICS AND USER INTRINSIC COMPONENTS " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
APA Style: C.Mani MCA., M.E., V.Gopalakrishnan " MUSIC RECOMMENDATON BASED ON MUSIC CHARACTERISTICS AND USER INTRINSIC COMPONENTS " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
Deep learning shows its superiority in many domains like computing vision, nature language processing, and speech recognition. In music recommendation, most deep learning-based methods specialise in learning users’ temporal preferences using their listening histories. The cold start problem isn't addressed, however, and therefore the music characteristics aren't fully exploited by these methods. additionally , the music characteristics and therefore the users’ temporal preferences aren't combined naturally, which cause the relatively low performance of music recommendation. to deal with these issues, we proposed a Deep Temporal Neural Music Recommendation model (DTNMR) supported music characteristics and therefore the users’ temporal preferences. We encoded the music metadata into one-hot vectors and utilized the Deep Neural Network to project the music vectors to low-dimensional space and acquire the music characteristics. additionally , Long STM (LSTM) neural networks are utilized to find out about users’ long-term and short-term preferences from their listening histories. DTNMR alleviates the cold start problem within the item side using the music metadata and discovers new users’ preferences immediately after they hear music. The experimental results show DTNMR outperforms seven baseline methods in terms of recall, precision, f-measure, MAP, user coverage and AUC.
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—— semantic music recommendation; deep neural network; cold start problem.