A symbolic hybrid approach to face the new user problem in recommender systems

Recommender Systems seek to furnish personalized suggestions automatically based on user preferences These preferences are usually expressed as a set of items either directly or indirectly given by the user (e.g., the set of products the user bought in a virtual store) In order to suggest new items, Recommender Systems generally use one of the following approaches: Content Based Filtering, Collaborative Filtering or hybrid filtering methods In this paper we propose a strategy to improve the quality of recommendation in the first user contact with the system Our approach includes a suitable plan to acquiring a user profile and a hybrid filtering method based on Modal Symbolic Data Our proposed technique outperforms the Modal Symbolic Content Based Filter and the standard kNN Collaborative Filter based on Pearson Correlation.