| dc.description.abstract | When music streaming service nearly becomes an essential application on mobile devices, people have thousands of choice about listening to music. Namely, music recommendation is an important service to retain customers. For content providers, they can gather click data by online streaming service to improve system performance. Therefore, music recommendation systems gradually prevail on account of the dependency relation for it between customers and content providers.
The influential factors of recommendation consist of social factors, geographic location and listening scenario from user preference as well as listening repeatedly behavior. One key problem is that the numbers of songs grow more rapidly than those of movies, which results in increasing difficulties to recommend songs accurately. Since the cost for listening music is low, the factors can be weak and fragmented which leads to complicated motivation. Comparing listening behavior with watching movies behavior, we find the former is unfixed and has low regularity. That is to say, listening behavior could not reflect perfectly the true interest to songs from users.
In this paper, we focus on the KKBOX to predict whether or not the recurring listening event will be triggered with a month. We design context features, user features and song features for combination with deep collaborative filtering. On our experiment, we compare matrix factorization models with the extended model (NCF, NeuMF). We combine features with matrix factorization extended model to improve the performance. Finally, we analyze the features factor and architecture from the best model (Lystdo[22]) in WSDM Cup 2018 music recommendation task. | en_US |