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In recent years, the emerging topics of recommender systems that takeadvantage of natural language processing techniques have attracted muchattention, and one of their applications is the Conversational RecommenderSystem (CRS). Unlike traditional recommender systems with content-based andcollaborative filtering approaches, CRS learns and models user's preferencesthrough interactive dialogue conversations. In this work, we provide asummarization of the recent evolution of CRS, where deep learning approachesare applied to CRS and have produced fruitful results. We first analyze theresearch problems and present key challenges in the development of DeepConversational Recommender Systems (DCRS), then present the current state ofthe field taken from the most recent researches, including the most common deeplearning models that benefit DCRS. Finally, we discuss future directions forthis vibrant area.

Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems