Image Recommendation System for Product Listing Completeness
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Solution Overview
Problem
Current online product sale systems lack the ability to provide useful guidance to sellers about relevant visual product details, as they cannot infer missing information and fail to detect omissions in product images, leading to incomplete product listings that may not attract buyers.
Innovation Solution
An image recommendation system that receives an initial product image set from a user, analyzes it to identify the product category and sub-category, and then retrieves and clusters images from digital marketplaces to provide recommended images that cover missing poses or orientations, allowing the user to upload additional images to enhance the completeness of their product listing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current image processing systems are used to detect and identify objects in product images, then object detection and pose prediction are achieved, but the system cannot infer missing information or detect omissions in product images
Solution Approach 1:
The system provides feedback to sellers by analyzing their product images and generating recommendations for additional images that would complete the product detail coverage. The system identifies what is missing from the product image set and suggests specific poses or views that should be captured, enabling sellers to improve their image sets based on actionable feedback.
Solution Approach 2:
The system performs preliminary analysis of the product images to identify missing poses and orientations before the seller finalizes their product listing. By proactively detecting what is missing and providing recommendations in advance, the system enables sellers to capture complete product information in subsequent imaging sessions.
2Ease of operation
If sellers provide minimal product images to reduce effort, then ease of operation is improved, but the completeness of visual product details deteriorates
Solution Approach 1:
The system automatically analyzes the product images provided by sellers and generates its own recommendations for additional images without requiring seller intervention. The system self-evaluates the completeness of the image set and autonomously identifies gaps, reducing the burden on sellers while ensuring comprehensive coverage.
Solution Approach 2:
The system provides sellers with automated feedback about their image completeness, showing them exactly what additional images would be beneficial. This feedback mechanism enables sellers to understand the value of adding more images without having to manually assess their own product photography quality.
3Ease of operation
If online selling platforms provide basic hosting and purchasing protocols, then ease of operation is improved, but the ability to provide guidance on visual product details deteriorates
Solution Approach 1:
The system acts as an intermediary between the seller and the buyer, providing automated analysis and recommendations that bridge the gap. It translates complex visual assessment requirements into simple, actionable recommendations for sellers, enabling platforms to provide sophisticated guidance without requiring manual expert review.
Solution Approach 2:
The platform integrates automated image analysis capabilities that serve the seller's needs independently. The system self-evaluates product images, identifies missing details, and generates recommendations automatically, allowing the platform to provide advanced guidance while maintaining ease of use for sellers.
Data Source
AI summary
Techniques are disclosed for generating image recommendations to facilitate the sale of a product. An example methodology includes identifying a product category associated with an image of the product provided by the seller, and a product sub-category associated with the product image. The method further includes retrieving one or more images of for-sale items. The retrieval is based on a search of for-sale listings using the identified product category and the identified product sub-category. The method further includes clustering the retrieved images of for-sale items into groups, each group associated with a perspective viewpoint of the for-sale item. The method further includes providing a selected image from each group as an image recommendation. The selection is based on a value score associated with each of the images of the for-sale items. A graphical status indicating completeness of the seller's image set is updated in response to recommended images being adopted.