  Conversational recommender systems (CRS) aim to employ natural language
conversations to suggest suitable products to users. Understanding user
preferences for prospective items and learning efficient item representations
are crucial for CRS. Despite various attempts, earlier studies mostly learned
item representations based on individual conversations, ignoring item
popularity embodied among all others. Besides, they still need support in
efficiently capturing user preferences since the information reflected in a
single conversation is limited. Inspired by collaborative filtering, we propose
a collaborative augmentation (COLA) method to simultaneously improve both item
representation learning and user preference modeling to address these issues.
We construct an interactive user-item graph from all conversations, which
augments item representations with user-aware information, i.e., item
popularity. To improve user preference modeling, we retrieve similar
conversations from the training corpus, where the involved items and attributes
that reflect the user's potential interests are used to augment the user
representation through gate control. Extensive experiments on two benchmark
datasets demonstrate the effectiveness of our method. Our code and data are
available at https://github.com/DongdingLin/COLA.
