This paper focuses on question selection methods for conversational recommender systems. We consider a scenario where given an initial user query, the recommender system may ask the user to provide additional features describing the searched products. The objective is to generate question/features that a user would likely reply, and if replied, would effectively reduce the result size of the initial query. Classical entropy-based feature selection methods are effective in term of result size reduction, but they select questions uncorrelated with user needs and therefore unlikely to be replied. We propose two featre-selection methods that combine feature entropy with an appropriate measure of feature relevance. We evaluated these methods in a set of simulated interacions where a probabilistic model of user behavior is exploited. The results show that these methods outperform entropy-based feature selection

Feature Selection Methods for Conversational Recommender Systems

Mirzadeh, Nader;Ricci, Francesco;
2005-01-01

Abstract

This paper focuses on question selection methods for conversational recommender systems. We consider a scenario where given an initial user query, the recommender system may ask the user to provide additional features describing the searched products. The objective is to generate question/features that a user would likely reply, and if replied, would effectively reduce the result size of the initial query. Classical entropy-based feature selection methods are effective in term of result size reduction, but they select questions uncorrelated with user needs and therefore unlikely to be replied. We propose two featre-selection methods that combine feature entropy with an appropriate measure of feature relevance. We evaluated these methods in a set of simulated interacions where a probabilistic model of user behavior is exploited. The results show that these methods outperform entropy-based feature selection
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/2399
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