Multi-head Self-attention Recommendation Model based on Feature Interaction Enhancement

In the recommendation system, click-through rate (CTR) prediction is a popular research direction. Aiming at the problem of excessive compression of features in Factorization Machine (FM) and its variant models, a recommendation model that combines feature interaction enhancement and multi-head self-attention is proposed. Hadamard product, feature vector splicing and multi-layer perception network methods are used for low-level feature vector interactive processing in this paper, and multi-head self-attention mechanism and residual network model for high-level feature interactive processing are used. By designing the fusion mechanism, the parallel low-order feature interaction network and the high-order feature interaction network are merged. The experimental results on the four benchmark data sets show that the multi-head self-attention model based on high-order feature interaction enhancement proposed in this paper outperforms existing models in terms of click-through rate prediction accuracy.