Commercial Site Recommendation Based on Neural Collaborative Filtering

Commercial site recommendation based on big data is one of the innovative applications in the new retail era. Recently, most studies utilize regression analysis or collaborative filtering to recommend the optimal site based on some features extracted from commercial data, geographic data and other heterogeneous data. Compared to manual features which could not be well-defined, deep learning is able to automatically extract features and give nonlinear and in-depth description of the relationship between variables. Therefore, this paper applies deep learning to the study of commercial site recommendation. We firstly study the usage of NeuMF, a neural collaborative filtering method in commercial site recommendation. Then we propose NeuMF-RS method based on NeuMF method. Finally, we evaluate our proposed model on a real-world dataset collected from Dianping. com. The results indicate that NeuMF-RS outperforms the state-of-the-art methods in commercial site recommendation.