An Efficient Scheme to Combine the User Demographics and Item Attribute for Solving Data Sparsity and Cold-start Problems

This paper investigates several schemes to combine the user demographic information and item attribute data that respectively beneficial to solve the data sparsity and cold-start problems in recommendation systems. We propose four schemes that are varied based on how the combination of the two data can be constructed. To test and evaluate the concept, we implement the schemes on a probabilistic-attribute method adapted to suit our attribute model. Compared to the benchmark methods, experiment results show that our approach is superior in solving the data sparsity and cold-start problems. In general, the scheme that combines the item attribute data with a partial user demographic information performs better than the other variations of the combined-attribute scheme. This finding confirms that combining both the user demographic information, though not all of them, and the item attribute can efficiently solve the data sparsity and cold-start problems.