Maximum Entropy for Collaborative Filtering
This work addresses data sparsity and input variability in collaborative filtering, but appears incremental as it adapts an existing method to a specific domain.
The paper tackles the challenges of sparse training data and varying user inputs in collaborative filtering by proposing a maximum entropy approach with a non-standard entropy measure, which simplifies to solving linear equations efficiently.
Within the task of collaborative filtering two challenges for computing conditional probabilities exist. First, the amount of training data available is typically sparse with respect to the size of the domain. Thus, support for higher-order interactions is generally not present. Second, the variables that we are conditioning upon vary for each query. That is, users label different variables during each query. For this reason, there is no consistent input to output mapping. To address these problems we purpose a maximum entropy approach using a non-standard measure of entropy. This approach can be simplified to solving a set of linear equations that can be efficiently solved.