A Dataset of Real Dialogues for Conversational Recommender Systems
Authors: Andrea Iovine, Fedelucio Narducci, Marco de Gemmis (Università di Bari Aldo Moro, 2019)
Topic: Chatbots and Dialogue Systems

Abstract
Conversational Recommender Systems (CoRS) that use natural language to interact with users usually need to be trained on large quantities of text data. Since the utterances used during the interaction with a CoRS may be different depending on the domain of the items, the system should also be trained separately for each domain. So far, there are no publicly available datasets based on real dialogues for training the components of a CoRS. In this paper, we propose three datasets that are useful for training a CoRS in the movie, book, and music domains. These datasets have been collected during a user study for evaluating a CoRS. They can be used to train several components, such as the Intent Recognizer, Entity Recognizer, and Sentiment Recognizer.

Introduction
Recommender Systems (RS) are software systems that help people make better decisions AUTHOR. They have become a fundamental tool for overcoming the information overloading problem, which is caused by the ever-increasing variety of information and products that people can access AUTHOR. Choosing between such a large quantity of options is not easy, and this results in a decrease in the quality of the decisions. Recommender systems help alleviate the problem by providing personalized suggestions to users, based on their preferences. Conversational Recommender Systems (CoRS) are a particular type of Recommender Systems, that acquire the user’s profile in an interactive manner AUTHOR. This means that, in order to receive a recommendation, the system does not require that all the information is provided beforehand, but it guides the user in an interactive, human-like dialog AUTHOR. Even though a CoRS can be implemented using several different interfaces, it is reasonable to think that an interaction based on natural language is suitable for the task. In particular, Digital Assistants (DA) such as Amazon Alexa, Google Assistant, or Apple’s Siri are interesting platforms to deliver recommendations in a conversational manner. DAs, popularized with the diffusion of smartphones, are able to help users complete everyday tasks through a conversation in natural language. However, there is still a technological gap between CoRSs and DAs, as described in AUTHOR. In particular, one of the main causes of that gap is the lack of labeled data. In fact, implementing a natural language-based interface for a CoRS is not easy, as it requires the use of several Natural Language Understanding (NLU) operations. For example, a basic conversational recommender needs at least three NLU components: an Intent Recognizer, an Entity Recognizer, and a Sentiment Analyzer. These components need to be trained on large quantities of real sentences, which may not always be available. The problem is worsened by the fact that each component may need to be trained separately for each different domain. In this paper, we present three datasets that contain utterances used in real dialogues between users and a CoRS respectively in the movie, book, and music domains. These datasets can then be used to train the components of a new CoRS. To the best of our knowledge, this is the first time such a dataset of real dialogues is provided for the book and music domains, while there is already one example for the movie domain AUTHOR. The dataset is available at the following link1 . Section 2 contains a literature review of datasets for training Question Answering and Conversational Recommender Systems. Section 3 illustrates the architecture of the CoRS that was used to collect the messages in the dataset. Section 4 describes in detail the three datasets, providing some statistics, and a small example of conversation.

Conclusion
In this paper, we presented three datasets that contain real user messages sent to Conversational Recommender Systems in the movie, book, and music domains. The datasets can be used to train a new CoRS to detect the intents, and with a few modifications, also to recognize entities and sentiments. The size of the data that we provide may not be sufficient to train deep learning-based End-to-End conversational recommendation models. However, this is outside the scope of our work: as stated in the previous sections, the aim of our datasets is to learn a conversational recommendation dialog model, independently from the actual recommendation algorithm. In any case, we believe that this is the first time that a dataset for training CoRSs in the book and music domain is released. Also, we believe that this is a good starting point for the release of further conversational datasets in multiple domains. We propose, as future work, to expand the datasets, by collecting more messages, in more domains. We will also explore the possibility to use our datasets to evaluate new CoRSs.