Resale Recommendation System Using CLIP Embeddings
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Solution Overview
Problem
Existing online shopping platforms face challenges in efficiently finding resale alternatives for fashion items and accessories, as standard reverse image search and semantic searching methods are inadequate, especially for constantly updated items, leading to time-consuming and inefficient results.
Innovation Solution
A recommendation system that uses a five-step process involving first-hand website extraction, data preparation, embeddings, ranking, and fusion to provide users with the best resale alternatives. This system employs metadata heuristics, Large Language Model (LLM) based extraction, intelligent image cropping, Contrastive Language-Image Pre-training (CLIP) model embeddings, and Approximate Nearest Neighbor (ANN) algorithms to unify data from resale partners and return streamlined results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard reverse image search and semantic searching are used, then the search process is simple to implement, but the search results are inadequate and time-consuming for fashion items that are constantly being updated
Solution Approach 1:
The search process is divided into multiple specialized stages: image preprocessing, feature extraction using CLIP model, semantic analysis, and result ranking. Each stage handles specific aspects of the search problem, allowing the system to maintain simplicity while improving accuracy for constantly updated fashion items
Solution Approach 2:
The system transforms the search approach by changing from standard reverse image search parameters to CLIP model embeddings that capture both visual and semantic features. This parameter transformation enables more accurate matching of fashion items that are frequently updated, as the embeddings encode contextual understanding beyond simple image similarity
2Device complexity
If existing search algorithms are used, then the implementation is straightforward, but the system is time and memory consuming for fashion items that are constantly being updated
Solution Approach 1:
The system performs preliminary actions by pre-processing images and generating CLIP embeddings before the actual search query is executed. Fashion items are pre-indexed with their visual and semantic features, so when a search is performed, the system can quickly compare against the pre-processed data without performing heavy computations in real-time, reducing search time while maintaining manageable system complexity
Solution Approach 2:
The patent replaces traditional mechanical search algorithms with AI-based CLIP model embeddings. This substitution transforms the search mechanism from rule-based matching to semantic understanding, which reduces time and memory consumption for constantly updated fashion items by capturing essential features in a more efficient representation
Data Source
AI summary
A system and method for an integrated subsystem that identifies resale consumer goods available for sale whereby picture or text descriptions of items of firsthand consumer goods may be used as an input or search query where a user may enter size, style, and color parameters for clothing and fashion items. The plug-in uses a web crawler to maintain a database of resale consumer goods for sale and may isolate a single clothing item from a clothed person to perform a search query for resale versions of that item as well as provided improved specifically tailored results based on what the user is looking for at that specific time.