Cold-Start Recommendations for Audio News Stories Using Matrix Factorization

We investigate a suite of recommendation algorithms for audio news listening applications. This domain presents several challenges that distinguish it from more commonly studied applications such as movie recommendations: (1) we do not receive explicit rating feedback, instead only observing when a user skips a story; (2) new stories arrive continuously, increasing the importance of making recommendations for items with few observations (the cold start problem); (3) story attributes have high dimensionality, making it challenging to identify similar stories. To address the first challenge, we formulate the problem as predicting the percentage of a story a user will listen to; to address the remaining challenges, we propose several matrix factorization algorithms that cluster users, n-grams, and stories simultaneously, while optimizing prediction accuracy. We empirically evaluate our approach on a dataset of 50K users, 26K stories, and 975K interactions collected over a five month period. We find that while simple models work well for stories with many observations, our proposed approach performs best for stories with few ratings, which is critical for the real-world deployment of such an application.