Recommendation Algorithm Based on Product Category Path

In recent years, due to the rise of deep learning, most researchers are trying to combine the traditional recommendation system with deep learning, however the traditional recommendation model is still suitable for most scenes or be widely used as an auxiliary model of deep learning recommendation model because of its strong interpretability, fast training and deployment. When the recommendation algorithm calculates the recommended goods for users, this paper establishes the category path of goods, then find the goods according to the category path of goods and recommend them. At the same time, according to the characteristics of the data obtained in this paper, the similarity of goods is calculated by the combination of TF-IDF and Jaccard similarity coefficient, so as to infer the list of goods recommended to users.