An Efficient Context-Aware Music Recommendation Based on Emotion and Time Context

The enormous growth of Internet facilities, the user finds difficulties in choosing the music based on their current mindset. The context-aware recommendation has turned out to be well-established technique, which recommends a music based on the mindset of the user in various contexts. To enhance the potential of music recommendation, the emotion and time interval are considered as the most important context. Emotion context is not explored due to the difficulty in acquisition of emotions from user's microblogs on the particular music. This paper proposes an algorithm to extract the emotions of a user from microblog during a different time interval and represented at different granularity levels. Each music piece crawled from online YouTube repository is represented in a triplet format: (User_id, Emotion_vector, Music_id). These triplet associations are considered for developing several emotion-aware techniques to provide music recommendations. Several trial of experimentation demonstrates that the proposed method with user emotional context enhances the recommendation performance in terms of hit rate, precision, recall, and F1-measure.