Real-time Karaoke Recommendations : Session-based Multi-Task Recommendations with Multivariate RNNs

There are more than 2,000 Karaoke stores in Japan and approximately 50 million users visit every year for a singing experience. For Karaoke, recommendations of the next songs play a significant role in leading customers to explore more songs and make the most of time in Karaoke. However, few recommendation functions are provided due to the difficulty and uniqueness of the Karaoke environment, in which unknown groups choose songs based on with whom they are sharing the Karaoke experience and the group mood; solid information of these factors is not available. In this paper, we propose a session-based multivariate recurrent neural network (RNN) to consider an occasion for a Karaoke store visit and any possible mood changes; these factors are assumed to be embedded in the sequential data. The model uses metadata from previously played songs, the conditions present, and performs several tasks that predict the songs and artists to be played at time t to t+n. It outperformed the item-kNN algorithm for prediction tasks (offline) of the next song by 127.94% regarding MAP@20 and the next artist by 142.8% regarding MAP@10. In addition, we analyzed the performance of the model for three types of groups, namely individuals, older groups, and younger groups that were manually categorized from played songs, and it outperformed the item-kNN algorithm by 89.59%, 483.11%, and 50.88% regarding MAP@20 respectively. The model also outperformed the baselines for all positions in a session, which demonstrates that it has the potential to quickly capture the preferences of a target group and to recognize mood changes from the sequence. We have launched a recommender system that is designed for Japanese Karaoke stores. The system has demonstrated that the model used in this system is also appliable to business applications due to its ability to handle vast numbers of requests in a reasonable time and enhance user experience by helping to choose more diverse songs faster.