  Collaborative filtering on user-item interaction graphs has achieved success
in the industrial recommendation. However, recommending users' truly fascinated
items poses a seesaw dilemma for collaborative filtering models learned from
the interaction graph. On the one hand, not all items that users interact with
are equally appealing. Some items are genuinely fascinating to users, while
others are unfascinated. Training graph collaborative filtering models in the
absence of distinction between them can lead to the recommendation of
unfascinating items to users. On the other hand, disregarding the interacted
but unfascinating items during graph collaborative filtering will result in an
incomplete representation of users' interaction intent, leading to a decline in
the model's recommendation capabilities. To address this seesaw problem, we
propose Feedback Reciprocal Graph Collaborative Filtering (FRGCF), which
emphasizes the recommendation of fascinating items while attenuating the
recommendation of unfascinating items. Specifically, FRGCF first partitions the
entire interaction graph into the Interacted & Fascinated (I&F) graph and the
Interacted & Unfascinated (I&U) graph based on the user feedback. Then, FRGCF
introduces separate collaborative filtering on the I&F graph and the I&U graph
with feedback-reciprocal contrastive learning and macro-level feedback
modeling. This enables the I&F graph recommender to learn multi-grained
interaction characteristics from the I&U graph without being misdirected by it.
Extensive experiments on four benchmark datasets and a billion-scale industrial
dataset demonstrate that FRGCF improves the performance by recommending more
fascinating items and fewer unfascinating items. Besides, online A/B tests on
Taobao's recommender system verify the superiority of FRGCF.
