A novel graph-based feature interaction model for click-through rate prediction

Click-through rate (CTR) prediction is a crucial issue in recommender systems. In addition, data sparsity is a notable challenge for recommender systems compared to other applications. To overcome it, many learning-based models are studied to model feature interactions and improve CTR prediction. However, current inflexible and non-explicit feature combination methods have limitations that hinder accurate prediction. To address this issue, we propose a sophisticated feature interaction model based on a graph and factorization machine (FM). In this model, each node in the graph corresponds to a raw feature, the edge and its weight between two nodes are determined by the learnable latent vectors in the FM. This interaction method integrates the flexible and explicit representative ability of the graph with the learnability of the FM. Furthermore, it can be combined with most learning-based CTR prediction models to improve their performance. To verify this viewpoint, we apply it to improve three prominent models, including one deep-forest-based model and two deep-learning-based models, and compare them with the state-of-the-art techniques. Experimental results show that they significantly outperform to the original ones, and are competitive with the comparison models.