Soliciting User Preferences in Conversational Recommender Systems via Usage-related Questions

• URL: http://arxiv.org/abs/2111.13463v1
• Date: Fri, 26 Nov 2021 12:23:14 GMT
• Title: Soliciting User Preferences in Conversational Recommender Systems via Usage-related Questions
• Authors: Ivica Kostric and Krisztian Balog and Filip Radlinski
• Abstract summary: We propose a novel approach to preference elicitation by asking implicit questions based on item usage. First, we identify the sentences from a large review corpus that contain information about item usage. Then, we generate implicit preference elicitation questions from those sentences using a neural text-to-text model.
• Score: 21.184555512370093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A key distinguishing feature of conversational recommender systems over traditional recommender systems is their ability to elicit user preferences using natural language. Currently, the predominant approach to preference elicitation is to ask questions directly about items or item attributes. These strategies do not perform well in cases where the user does not have sufficient knowledge of the target domain to answer such questions. Conversely, in a shopping setting, talking about the planned use of items does not present any difficulties, even for those that are new to a domain. In this paper, we propose a novel approach to preference elicitation by asking implicit questions based on item usage. Our approach consists of two main steps. First, we identify the sentences from a large review corpus that contain information about item usage. Then, we generate implicit preference elicitation questions from those sentences using a neural text-to-text model. The main contributions of this work also include a multi-stage data annotation protocol using crowdsourcing for collecting high-quality labeled training data for the neural model. We show that our approach is effective in selecting review sentences and transforming them to elicitation questions, even with limited training data. Additionally, we provide an analysis of patterns where the model does not perform optimally.

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