Product-Recommendation-System

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Product-recommendation-system is an open-source AI tool that implements a user-based collaborative filtering algorithm for product recommendations. It uses cosine similarity to calculate user similarity and suggest products.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is product-recommendation-system?

Product-recommendation-system is an open-source project hosted on GitHub that provides a solution for product recommendations using a user-based collaborative filtering algorithm. It helps users navigate vast product information by recommending items based on preferences, age, click history, and purchase behavior. The system employs cosine similarity to measure the similarity between users, enabling it to recommend products viewed by similar users. Key features include user similarity calculation, recommendation of second-level categories, and final product recommendations. The project is built with Java, Spring, SpringMVC, Mybatis, and MySQL, making it a technical solution for developers looking to implement recommendation systems.

Best used for

Ideal for developers and data scientists who need to implement a product recommendation system, understand collaborative filtering algorithms, and build personalized shopping experiences. Especially valuable for those looking for an open-source Java-based solution.

Common actions

build recommendation system

implement collaborative filtering

learn AI algorithms

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Capabilities

Key features

User similarity calculation
Collaborative filtering algorithm
Product recommendation
Category recommendation
Cosine similarity

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What programming languages and frameworks are used in this recommendation system?

The product recommendation system is primarily developed using Java 1.8, with Spring 4.2.1 for the backend framework, SpringMVC 4.2.1 for the presentation layer, and Mybatis 3.3.1 for database interaction. MySQL 5.6.36 is used as the database, and Maven for project building.

How does the system calculate user similarity for recommendations?

The system calculates user similarity using the cosine similarity metric. This method assesses the angle between two user vectors, where each vector represents a user's interactions (like click counts) with different products. A smaller angle, resulting in a cosine value closer to 1, indicates higher similarity between users.

What is the core recommendation algorithm implemented in this project?

The core recommendation algorithm implemented in this project is user-based collaborative filtering. This approach identifies users with similar preferences and behaviors, then recommends products that those similar users have interacted with but the target user has not yet seen.

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