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To hold a true conversation, an intelligent agent should be able tooccasionally take initiative and recommend the next natural conversation topic.This is a challenging task. A topic suggested by the agent should be relevantto the person, appropriate for the conversation context, and the agent shouldhave something interesting to say about it. Thus, a scripted, orone-size-fits-all, popularity-based topic suggestion is doomed to fail.Instead, we explore different methods for a personalized, contextual topicsuggestion for open-domain conversations. We formalize the Conversational TopicSuggestion problem (CTS) to more clearly identify the assumptions andrequirements. We also explore three possible approaches to solve this problem:(1) model-based sequential topic suggestion to capture the conversation context(CTS-Seq), (2) Collaborative Filtering-based suggestion to capture previoussuccessful conversations from similar users (CTS-CF), and (3) a hybrid approachcombining both conversation context and collaborative filtering. To evaluatethe effectiveness of these methods, we use real conversations collected as partof the Amazon Alexa Prize 2018 Conversational AI challenge. The results arepromising: the CTS-Seq model suggests topics with 23% higher accuracy than thebaseline, and incorporating collaborative filtering signals into a hybridCTS-Seq-CF model further improves recommendation accuracy by 12%. Together, ourproposed models, experiments, and analysis significantly advance the study ofopen-domain conversational agents, and suggest promising directions for futureimprovements.

Would you Like to Talk about Sports Now? Towards Contextual Topic Suggestion for Open-Domain Conversational Agents