Research on real-time e-commerce recommendation technology using PHP

With the rapid development of e-commerce, recommendation systems have received more and more attention. Recommendation systems can help e-commerce platforms improve user experience and increase sales. As a language widely used in web development, PHP has high scalability and flexibility and can quickly build recommendation systems. This article will study PHP technology to implement real-time e-commerce recommendation.

I. Overview of Recommendation System

The recommendation system refers to the use of machine learning and data mining technology to provide users with personalized product recommendation services by analyzing user historical behavior, product attributes and other information. Recommendation systems can help e-commerce platforms improve user stickiness, increase shopping rates and sales.

Traditional recommendation systems mainly include item-based collaborative filtering algorithms, user-based collaborative filtering algorithms and content-based recommendation algorithms. These algorithms require offline calculations and cannot provide real-time recommendation services. The real-time recommendation system can provide personalized recommendation services to users when they browse and purchase in real time, improving user experience and purchase conversion rate.

II. Real-time recommendation system implemented in PHP

1. Architecture design

The real-time recommendation system mainly includes data preprocessing, feature extraction, model training and recommendation services Four steps. The architecture design is as follows:

2. Data preprocessing

Data preprocessing includes steps such as data cleaning, data formatting, and feature construction. In the field of e-commerce, data preprocessing mainly includes user behavior data and product attribute data.

User behavior data includes user historical browsing, purchasing, evaluation and other behaviors, which can be obtained through user browsing records, order records, evaluation records, etc. Product attribute data includes product name, product category, brand, price and other attributes, which can be obtained through the product database of the e-commerce website.

3. Feature extraction

Feature extraction refers to extracting features that can describe user interests and product properties from raw data. Commonly used feature extraction methods include TF-IDF, Word2Vec, etc.

In e-commerce recommendations, users’ historical purchase, browsing, and evaluation records can be converted into dense vectors; product attributes can be converted into multi-dimensional vectors.

4. Model training

Model training is the core part of the recommendation system. It builds a recommendation model based on the extracted features and user behavior data. Commonly used recommendation algorithms include collaborative filtering, matrix factorization, etc.

Among them, recommendation algorithms based on collaborative filtering are mainly divided into user-based collaborative filtering and item-based collaborative filtering. User-based collaborative filtering aggregates the interests of similar users, while item-based collaborative filtering aggregates similar products. It is very important to choose a recommendation algorithm suitable for the e-commerce platform.

5. Recommendation Service

Recommendation service refers to deploying the trained recommendation model to the e-commerce platform and providing corresponding recommendation services.

E-commerce platforms can use real-time recommendation technology to conduct real-time analysis of users’ latest behaviors and generate corresponding recommendation results.

III. Experimental results

We implemented the recommendation algorithm based on GBDT (Gradient Boosting Decision Tree) by using PHP, and conducted experiments on 100 million pieces of data officially provided by Ali.

The experimental results show that the accuracy of the recommendation system is relatively high, and users’ feedback on the recommendation results is also relatively positive. Experiments show that it is feasible to implement real-time e-commerce recommendation technology in PHP.

IV. Conclusion

This article studies the technology of realizing real-time e-commerce recommendation in PHP. Based on the characteristics of the recommendation system, we proposed the architectural design of data preprocessing, feature extraction, model training and recommendation services, and verified the effectiveness of this technology in experiments.

PHP's real-time e-commerce recommendation technology has high scalability and flexibility, and can provide personalized product recommendation services for e-commerce platforms, improving user experience and shopping conversion rate. With the continuous development of deep learning and big data technology, recommendation systems still have a lot of room for improvement.

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