Meta Policy Learning for Cold-Start Conversational Recommendation
- URL: http://arxiv.org/abs/2205.11788v1
- Date: Tue, 24 May 2022 05:06:52 GMT
- Title: Meta Policy Learning for Cold-Start Conversational Recommendation
- Authors: Zhendong Chu, Hongning Wang, Yun Xiao, Bo Long, Lingfei Wu
- Abstract summary: We study CRS policy learning for cold-start users via meta reinforcement learning.
To facilitate policy adaptation, we design three synergetic components.
- Score: 71.13044166814186
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Conversational recommender systems (CRS) explicitly solicit users'
preferences for improved recommendations on the fly. Most existing CRS
solutions employ reinforcement learning methods to train a single policy for a
population of users. However, for users new to the system, such a global policy
becomes ineffective to produce conversational recommendations, i.e., the
cold-start challenge.
In this paper, we study CRS policy learning for cold-start users via meta
reinforcement learning. We propose to learn a meta policy and adapt it to new
users with only a few trials of conversational recommendations. To facilitate
policy adaptation, we design three synergetic components. First is a
meta-exploration policy dedicated to identify user preferences via exploratory
conversations. Second is a Transformer-based state encoder to model a user's
both positive and negative feedback during the conversation. And third is an
adaptive item recommender based on the embedded states. Extensive experiments
on three datasets demonstrate the advantage of our solution in serving new
users, compared with a rich set of state-of-the-art CRS solutions.
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