A symbolic approach for content-based information filtering

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Abstract

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.

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