Author: Zen and the Art of Computer Programming
1 Introduction
In the field of e-commerce, recommendation system (Recommendation System) is a long-standing and very important issue. Its main role is to mine the potential preferences of users in a large number of user interest data and provide suitable products or services, thereby increasing the willingness to purchase goods and services, increasing economic benefits and social satisfaction. However, in practical applications, content-based recommendation algorithms often show good results, but they also face some defects. For example, they tend to ignore complex factors such as users' historical behavior and changes in interest preferences, resulting in unstable recommendation effects and low prediction accuracy. Therefore, how to better deal with these problems is one of the current research hotspots.
In this context, a new deep learning-based click rate prediction model is proposed - Factorization Machine ( FM ) based Neural Network (DeepFM). It can effectively capture the interaction information between different features and overcome the shortcomings of traditional matrix factorization models. In addition, this model takes into account the advantages of both the deep network structure and the linear model, so it can well solve the cold start problem in the recommendation system. Finally, by comparing different CTR prediction models, the performance of DeepFM can reach the state-of-the-art.
This paper first introduces the basic knowledge related to the recommendation system, and then elaborates on the main characteristics of the DeepFM model, including:
- Use FM to capture interaction information between features;
- Use DNN for deep feature fusion;
- The regularization term of FM is improved to make the model easier to generalize.
Next, the author will describe the specific implementation process of the DeepFM model in detail, and show the derivation and code implementation of the mathematical formula. Finally, the author analyzes and compares the performance of the DeepFM model, demonstrating its differences from traditional models.
The purpose of this article is to