Recommendation via Entering a Human Conversation

Abstract

Abstract We propose a novel way of the conversational recommendation where instead of asking user questions to acquire her preferences, the recommender tracks her conversations with other people, including customer support agents (CSA) and joins the conversation only when there is something important to recommend and the time is correct to do so. Building a recommender that joins a human conversation (CRS), we propose information extraction, discourse and argumentation analyses, as well as dialogue management techniques to compute a recommendation for a product and service that is badly needed by the customer, as inferred from the conversation. A special case of such conversations is considered where the customer raises his problem with a CSA in an attempt to resolve at, along with receiving a recommendation for a product with features addressing this problem. Performance of CRS is evaluated in a number of human-human and human-chatbot dialogues and demonstrates that CRS is efficient and less intrusive way to provide high relevance and persuasive recommendations.