Predicting Consumption Patterns with Repeated and Novel Events

There are numerous contexts where individuals typically consume a few items from a large selection of possible items. Examples include purchasing products, listening to music, visiting locations in physical or virtual environments, and so on. There has been significant prior work in such contexts on developing predictive modeling techniques for recommending new items to individuals, often using techniques such as matrix factorization. There are many situations, however, where making predictions for both previously-consumed and new items for an individual is important, rather than just recommending new items. We investigate this problem and find that widely-used matrix factorization methods are limited in their ability to capture important details in historical behavior, resulting in relatively low predictive accuracy for these types of problems. As an alternative we propose an interpretable and scalable mixture model framework that balances individual preferences in terms of exploration and exploitation. We evaluate our model in terms of accuracy in user consumption predictions using several real-world datasets, including location data, social media data, and music listening data. Experimental results show that the mixture model approach is systematically more accurate and more efficient for these problems compared to a variety of state-of-the-art matrix factorization methods.