Towards Conversational Recommendation over Multi-Type Dialogs

We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user’s interests and feedback.

To address this challenge, we present a novel task, conversational recommendation over multitype dialogs, where we want the bot to proactively and naturally lead a conversation from a non-recommendation dialog to a recommendation dialog. For example, in Figure 1, given a starting dialog such as question answering, the bot can take into account user’s interests to determine a recommendation target (the movie the message as a long-term goal, and then drives the conversation in a natural way by following short-term goals, and completes each goal in the end. Here each goal specifies a dialog type and a dialog topic. Our task setting is different from previous work (Christakopoulou et al., 2016; Li et al., 2018). First, the overall dialog in our task contains multiple dialog types, instead of a single dialog type as done in previous work. Second, we emphasize the initiative of the recommender, i.e. the bot proactively plans a goal sequence to lead the dialog, and the goals are unknown to the users. When we address this task, we will encounter two difficulties: (1) how to proactively and naturally lead a conversation to approach the recommendation target, (2) how to iterate upon initial recommendation with the user.