CAMEO: Fostering Joint Conversational Search and Recommendation

The rising popularity of conversational agents for accessing information stems from their natural language dialogue-based interaction, facilitating usability for a broad spectrum of users, including the elderly, children, and visually impaired individuals. Among others, two tasks that benefit the most conversational agents are search and recommendation: in the former, the user receives factual information by asking the agent; in the latter, the system refines its knowledge of the user's needs by posing them a sequence of questions. This work discusses the observations and findings of the first CAMEO (Conversational Agents: Mastering, Evaluating, Optimizing) project retreat. The retreat focused on similarities and differences of conversational search and recommendation to identify the path to construct a joint conversational search and recommendation system. Our observations highlight how all the conversational search/recommendation systems can be categorized using two axes: “exploration-disambiguation” and “search-recommendation”. The first axis describes whether the question aims to gain knowledge over something unknown or allows to refine already available knowledge. The second axis describes if the user's interest is in gaining knowledge or obtaining a recommendation. Additionally, we provide insights on obtaining a dataset that can be used to train/test such a joint system. Finally, we describe how the CAMEO project will address the product search task, which we believe is the scenario where the joint conversational search and recommendation system would be the most effective.