A Lattice-Based Model for Recommender Systems

A major challenge in building recommender systems is organizing the recommendation space since, the underlying data organization scheme has a significant impact on the overall performance and capabilities of the recommender system. Lattices offer a natural scheme to encode the recommender space, and their structural properties could be leveraged to efficiently retrieve recommendations. In our lattice-based approach, we present a generic framework and algorithms for building recommender systems. The algorithms convert user ratings into concepts; organize concepts into coherent lattices by imposing constraints based on collaborative filtering, and enable fast querying of lattices for generating recommendations. A lattice-based model also offers interesting insights into the complex higher-level interrelationships between entities in the data. We apply our algorithms on two real-world datasets and demonstrate their capabilities in generating quality recommendations in real-time.