  Click-through rate prediction is a critical task in online advertising.
Currently, many existing methods attempt to extract user potential interests
from historical click behavior sequences. However, it is difficult to handle
sparse user behaviors or broaden interest exploration. Recently, some
researchers incorporate the item-item co-occurrence graph as an auxiliary. Due
to the elusiveness of user interests, those works still fail to determine the
real motivation of user click behaviors. Besides, those works are more biased
towards popular or similar commodities. They lack an effective mechanism to
break the diversity restrictions. In this paper, we point out two special
properties of triangles in the item-item graphs for recommendation systems:
Intra-triangle homophily and Inter-triangle heterophiy. Based on this, we
propose a novel and effective framework named Triangle Graph Interest Network
(TGIN). For each clicked item in user behavior sequences, we introduce the
triangles in its neighborhood of the item-item graphs as a supplement. TGIN
regards these triangles as the basic units of user interests, which provide the
clues to capture the real motivation for a user clicking an item. We
characterize every click behavior by aggregating the information of several
interest units to alleviate the elusive motivation problem. The attention
mechanism determines users' preference for different interest units. By
selecting diverse and relative triangles, TGIN brings in novel and
serendipitous items to expand exploration opportunities of user interests.
Then, we aggregate the multi-level interests of historical behavior sequences
to improve CTR prediction. Extensive experiments on both public and industrial
datasets clearly verify the effectiveness of our framework.
