Improving Case Representation and Case Base
Maintenance in Recommender Agents

Miquel Montaner, Beatriz Lpez, and Josep Llus de la Rosa

Institut dInformtica i Aplicacions
Universitat de Girona
Campus Montilivi
17071 Girona, Spain
{mmontane, blopez, pepiluis}@eia.udg.es




Abstract. Recommendations by salespeople are always based on knowledge 
about the products and expertise about your tastes, preferences, interests 
and behavior in the shop. In an attempt to model the behavior of
salespeople, AI research has been focussed on the so called recommender
agents. Such agents draw on previous results from machine learning and
other advances in AI technology to develop user models and to anticipate 
and predict user preferences. In this paper we introduce a new
approach to recommendation, based on Case-Based Reasoning (CBR).
CBR is a paradigm for learning and reasoning through experience, as
salesmen do. We present a user model based on cases in which we try
to capture both explicit interests (the user is asked for information) and
implicit interests (captured from user interaction) of a user on a given
item. Retrieval is based on a similarity function that is constantly tuned
according to the user model. Moreover, in order to cope with the utility
problem that current CBR system suffer from, our approach includes a
forgetting mechanism (the drift attribute) that can be extended to other
applications beyond e-commerce.
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