Hierarchical Object Indexing With Multimodal Product Clustering
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Solution Overview
Problem
Existing e-commerce search methods require traversing large candidate product spaces, leading to reduced retrieval efficiency and accuracy due to the reliance on traditional indexing methods that do not effectively utilize multimodal features of products.
Innovation Solution
Establish an object index by obtaining multimodal features, performing multi-level clustering, and generating a hierarchical object index value for each product, using methods like BERT and BLIP models to enhance indexing efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional keyword-based or vector-based retrieval algorithms are used, then product retrieval can be performed, but retrieval efficiency is significantly reduced when candidate product space is large
Solution Approach 1:
The patent segments the candidate product space into hierarchical clusters based on multimodal features. Instead of treating all products as a single large space, the system divides products into multiple clusters at different levels, allowing the retrieval process to navigate through clusters rather than traversing the entire product space, thereby improving retrieval efficiency.
Solution Approach 2:
The patent transforms product representation by encoding multimodal features (images, text, attributes) into hierarchical cluster identifiers. This parameter transformation converts the original high-dimensional multimodal feature space into a compressed hierarchical structure that maintains semantic relationships while reducing retrieval complexity.
2Productivity
If traditional indexing methods are used, then product database can be searched, but indexing efficiency and search accuracy are insufficient
Solution Approach 1:
The patent creates a composite index structure that integrates multiple types of features (visual, textual, attribute) into a unified hierarchical clustering system. This composite approach combines the strengths of different feature types to achieve both efficient indexing and accurate search, overcoming the limitations of single-feature indexing methods.
Solution Approach 2:
The patent adds a hierarchical clustering dimension to the traditional flat index structure. By organizing products into multiple levels of clusters based on multimodal similarities, the system creates a new dimensional organization that improves both indexing efficiency and search accuracy simultaneously.
3Measurement precision
If candidate products are filtered through retrieval algorithms, then target products can be obtained, but the process is time-consuming and inefficient
Solution Approach 1:
The patent performs preliminary clustering of products into hierarchical groups based on multimodal features before the actual search occurs. This pre-organization allows the retrieval system to quickly narrow down candidate products by navigating through pre-computed clusters, significantly reducing search time while maintaining high retrieval accuracy.
Data Source
AI summary
The present application provides a method for establishing an object index, a method for training a prediction model, and a search method. The method for establishing an object index includes: obtaining multimodal features of each object under a target leaf category; performing multi-level clustering on the objects under the target leaf category based on the multimodal features to generate an object clustering hierarchy diagram; and establishing an object index value for each of the objects based on the object clustering hierarchy diagram. The object index value obtained through this method integrate richer object information, providing stronger indexing capabilities for objects and significantly improving indexing efficiency.