An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A symbolic approach for content-based information filtering
Information Processing Letters
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
International Journal of Approximate Reasoning
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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.