Triangle Graph Interest Network for Click-through Rate Prediction

• URL: http://arxiv.org/abs/2202.02698v1
• Date: Sun, 6 Feb 2022 03:48:52 GMT
• Title: Triangle Graph Interest Network for Click-through Rate Prediction
• Authors: Wensen Jiang, Yizhu Jiao, Qingqin Wang, Chuanming Liang, Lijie Guo, Yao Zhang, Zhijun Sun, Yun Xiong, Yangyong Zhu
• Abstract summary: We propose a novel framework named Triangle Graph Interest Network (TGIN) for click-through rate prediction. For each clicked item in user behavior sequences, we introduce the triangles in its neighborhood of the item-item graphs as a supplement. We characterize every click behavior by aggregating the information of several interest units to alleviate the elusive motivation problem.
• Score: 10.442084155281178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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