Feature Selection Methods for Conversational Recommender Systems

Authors:
Nader Mirzadeh;Francesco Ricci;Mukesh Bansal
Affiliations:
ITC-irst, Trento, Italy;ITC-irst, Trento, Italy;TIGEM, Napoli, Italy
Venue:
EEE '05 Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05) on e-Technology, e-Commerce and e-Service
Year:
2005

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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 questions/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 feature-selection methods that combine feature entropy with an appropriate measure of feature relevance. We evaluated these methods in a set of simulated interactions where a probabilistic model of user behavior is exploited. The results show that these methods outperform entropy-based feature selection.