Looking back on my technical career, from the recommendation system of Flying Pig to the AI products of Visual Travel Technology, this is not only the evolution of technology, but also an in-depth consideration of the essence of the application of algorithms.

# The Age of Recommendation Systems

During my work at Flying Pig, I was responsible for building the Goku Universal Recommendation System.This system supports thousands of recommended scenarios, from daily modules to double 11 promotions, from product recommendations to content recommendations.The core of the recommendation system is to understand the user's intention and push the most suitable content to the user through the algorithm.

In this process, I deeply realized that the recommendation system is not only an algorithmic problem, but also a system engineering problem.Need to deal with multiple aspects such as real-time features, offline training, A/B testing, effect evaluation, etc.Every optimization requires data backing, and every iteration requires business validation.

# AI Product Challenges

After coming to Vision Travel Technology, I began to take charge of the research and development of AI products.Unlike recommendation systems, AI products require stronger generalization capabilities and smarter decision making.We need to understand not only the user, but also the scenario and the business logic.

The challenge for AI products is how to turn algorithmic power into product value.This requires us to find a balance between algorithm accuracy, system performance, and user experience.We should pay attention not only to the accuracy of the model, but also to the performance of the product in the actual scenario.

# Reflections on technological evolution

From recommendation systems to AI production