Searching and recommending products in e-commerce with a large user base is an important and complex task. With millions of products and thousands of users accessing the website simultaneously, providing a robust search system and relevant product recommendations is crucial to meet users' needs and preferences.
For product search, e-commerce websites need to build a high-performance search system that allows users to easily find the products they are interested in. The search system should support keyword search, filtering by category, price range, ratings, and other product attributes.
To offer relevant product recommendations, e-commerce platforms can utilize the following methods:
Purchase history
Based on users' purchase history to recommend similar or relevant products that align with their preferences.
Behavior-based recommendations
Tracking users' behavior on the website, such as viewing product pages or adding items to the cart, and suggesting similar or related products.
User data analysis
Using user data to understand their shopping behavior and preferences, and consequently proposing suitable products.
Community filtering
Leveraging user ratings, comments, and likes from the community to recommend popular and favored products.
Machine learning and artificial intelligence
Applying machine learning algorithms and artificial intelligence to optimize the product recommendation system and improve accuracy.
Combining these methods helps e-commerce websites offer a better shopping experience and assists users in easily finding products that match their needs and preferences.