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Reinforcement Learning Based Conversational Search Assistant

by   Milan Aggarwal, et al.

In this work, we develop an end-to-end Reinforcement Learning based architecture for a conversational search agent to assist users in searching on an e-commerce marketplace for digital assets. Our approach caters to a search task fundamentally different from the ones which have limited search modalities where the user can express his preferences objectively. The system interacts with the users to display search results to the queries, and gauges user's intent and context of the conversation to choose the next action and reply. To train the agent in the absence of true conversation data, a virtual user is constructed to model a human user using the query and session logs from a major stock photography and digital assets marketplace. The system provides an alternative that is more engaging than the traditional search while maintaining similar effectiveness. This work provides a mechanism to build and deploy bootstrapped version of an effective conversational agent from readily available query log data. The system can then be used to acquire true conversational data and be fine-tuned further. The methodology discussed in this paper can be extended to e-commerce domains in general.


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