A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains

We develop a maximum entropy (maxent) approach to generating recommendations inthe context of a user's current navigation stream, suitable for environments where data is sparse, high-dimensional, and dynamic-- conditions typical of many recommendation applications. We address sparsity and dimensionality reduction by first clustering items based on user access patterns so as to attempt to minimize the apriori probability thatrecommendations will cross cluster boundaries and then recommending onlywithin clusters. We address the inherent dynamic nature of the problem by explicitly modeling the data as a time series; we show how this representational expressivity fits naturally into a maxent framework.