Research Area:  Machine Learning

Abstract:

Conversational Recommender Systems (CoRSs) implement a paradigm where users can interact with the system for defining their preferences and discovering items that best fit their needs. A CoRS can be straightforwardly implemented as a chatbot. Chatbots are becoming more and more popular for several applications like customer care, health care, medical diagnoses. In the most complex form, the implementation of a chatbot is a challenging task since it requires knowledge about natural language processing, human-computer interaction, and so on. In this paper, we propose a general framework for making easy the generation of conversational recommender systems. The framework, based on a content-based recommendation algorithm, is independent from the domain. Indeed, it allows to build a conversational recommender system with different interaction modes (natural language, buttons, hybrid) for any domain. The framework has been evaluated on two state-of-the-art datasets with the aim of identifying the components that mainly influence the final recommendation accuracy.

Keywords:  
Conversational Recommender Systems
CoRS
Chatbot
Recommendation accuracy
Natural language

Author(s) Name:  Fedelucio Narducci, Pierpaolo Basile, Andrea Iovine

Publisher name:  ACM

DOI:  

Volume Information:  

Paper Link:   https://ceur-ws.org/Vol-2290/kars2018_paper6.pdf