Enhancing user experience: a content-based recommendation approach for addressing cold start in music recommendation

Recommendation systems play a major role in modern music streaming platforms, assisting consumers in finding new music that suits their tastes. However, a significant challenge persists when it comes to recommending new songs that lack historical data. This study introduces a Content based Attentive Sequential Recommendation Model (CASRM) that deals with item cold start issue and recommends relevant and fresh music using Gated Graph Neural Networks (GNNs). Music metadata such as artists, albums, genres, and tags are included in the content information, along with context data incorporating user behaviour such as sessions, listening logs, and music playing sequences. By representing the music data as a graph, we can effectively capture the intricate relationships between songs and users. To capture users' music preferences, we analyse their interactions with songs within the sessions. We incorporate content-based item embeddings for newly added items, enabling personalized recommendations for new songs based on their characteristics and similarities to the songs listened by users in the past. Specifically, we examined the proposed model on three distinct datasets, and the experimental outcomes show its efficacy in predicting music ratings for new songs. Compared to other baseline methods, the CASRM model achieves superior performance in providing accurate and diverse music recommendations in cold-start scenarios.