Explainable Neural Tensor Factorization for Commercial Alley Revenues Prediction.

Many individuals aspire to start their own businesses and achieve financial success. Before launching a business, they must decide on a location and the type of service to offer. This decision requires collecting and analyzing various characteristics of potential locations and services, such as average revenues and foot traffic. However, this process is challenging because it demands expert knowledge in data collection and analysis. To address this issue, we propose Neural Tensor Factorization (NeuralTF) and Explainable Neural Tensor Factorization (XNeuralTF). These methods automatically analyze these characteristics and predict revenues. NeuralTF integrates Tensor Factorization (TF) with Multi-Layer Perceptron (MLP). This integration allows it to handle multi-dimensional tensors effectively. It also learns both explicit and implicit higher-order feature interactions, leading to superior predictive performance. XNeuralTF extends NeuralTF by providing explainable recommendations for three-dimensional tensors. Additionally, we introduce two novel metrics to evaluate the explainability of recommendation models. We conducted extensive experiments to assess both predictive performance and explainability. Our results show that XNeuralTF achieves comparable or superior performance to state-of-the-art methods, while also offering the highest level of explainability. [ABSTRACT FROM AUTHOR]