Modern Information Retrieval
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Developing a semantic-enable information retrieval mechanism
Expert Systems with Applications: An International Journal
A symbolic hybrid approach to face the new user problem in recommender systems
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Enhancement of information seeking using an information needs radar model
Information Processing and Management: an International Journal
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Recommender systems seek to furnish personalized suggestions automatically based on user preferences. These systems use information filtering techniques to recommend new items by comparing them with a user profile. This paper presents an approach through which each user profile is modelled using a set of modal symbolic descriptions that summarize the information taken from a set of items the user has previously evaluated. The comparison between a new item and a user profile is accomplished by way of a new suitable dissimilarity function that takes content and position differences into account. This new approach is evaluated by comparing it with a common information filtering technique: the standard kNN method.