Understanding Image-Based Recommendation Systems
Image recommendation systems are designed to make our shopping experience better by providing personalized product suggestions based on visual data. Instead of just categorizing images, these systems focus on finding visually similar items to a given product image. They use large datasets, which includes around 44,000 images across 143 different categories such as T-shirts, jeans, and watches.
Key Techniques in Image Recommendation Systems
Feature Extraction Methods:
- To extract low-level features from images, techniques like HSV histogram, edge detection, image texture analysis, and Histogram of Oriented Gradients (HOG) are used.
- For more complex features, advanced deep learning models such as VGG, Resnet, MobileNet, DenseNet, and Inception networks are employed.
Classification and Retrieval:
- The CNN Classifier Based Retrieval (CCBR) technique classifies an input image and then generates product recommendations based on the predicted class.
- Some systems combine VGG and Resnet models to compute cosine similarity between images, which helps recommend the top K similar products.
Image Based Product Recommendation System
Recommender systems in online shopping help us deal with information overload by using both implicit and explicit user data, as well as internal system insights, to guide us towards the best product choices. Plus, these systems rely on detailed product catalogs and use images to turn potential buyers into loyal customers.
Image-based recommendation systems take this a step further by using visual similarities between items to improve product visibility, scalability, and performance. They seamlessly integrate with existing e-commerce platforms and aim to enhance the user experience and boost revenue by offering personalized recommendations and increasing business visibility.
Table of Content
- Understanding Image-Based Recommendation Systems
- Key Techniques in Image Recommendation Systems
- Building Image-Based Product Recommendation Systems
- Image-based recommendation systems
- Step 1: Importing Libraries
- Step 2: Load Image Data:
- Step 3: Load and Prepare the Model
- Step 4: Feature Extraction Function
- Step 5: Extract features from all images
- Step 6: Save Features and Filenames
- Step7: Load Features and Filenames
- Step 8: Initialize Nearest Neighbors Model
- Step 9: Extract Features from single input image
- Step 10:Define Recommendation Function with GUI
- Step 11 :Example Usage
- Applications of Image Recommendation Systems
- Conclusion