We describe a recommender system based on dynamically structured holographic memory (DSHM), a cognitive model of associative memory that uses holographic reduced representations as the basis for its encoding of object associations. We compare this recommender to a conventional user-based collaborative filtering algorithm on three datasets: MovieLens, and two bibliographic datasets such as those typically found in a digital library. Off-line experiments show that the holographic recommender is competitive in accuracy for predicting movie preferences and more accurate than collaborative filtering on very sparse data sets. However, DSHM requires significant amounts of computational resources which may require a distributed implementation for it to be practical as a recommender for large data sets.