Fitment Data Clustering for Interchangeable Parts Retrieval
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Solution Overview
Problem
Existing item retrieval systems on listing platforms struggle to efficiently identify and present interchangeable parts due to the absence of manufacturer part numbers (MPNs) and missing associations between different MPNs, leading to increased computing resource consumption and user frustration.
Innovation Solution
An item retrieval system uses a machine learning model to generate item embeddings based on fitment data, clustering interchangeable parts based on similarity scores and constraints, and assigns cluster identifiers for efficient retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional item retrieval systems are used without clustering, then all part items must be processed and presented individually, but this leads to increased computing resource consumption and user frustration due to repetitive queries
Solution Approach 1:
The system performs preliminary clustering of part items into interchangeable groups before actual retrieval operations. Cluster data is pre-computed and stored, associating cluster identifiers with item listing identifiers. When a query is received, the system identifies the relevant cluster and retrieves items from that cluster, avoiding the need to process all items individually for each query.
2Measurement precision
If detailed fitment data processing is performed for each part item, then accurate interchangeable part identification is achieved, but this increases data processing complexity and time
Solution Approach 1:
Fitment data processing is performed in advance during the clustering phase. The embedding model processes item data including fitment data, item titles, descriptions, and structured attributes to generate embeddings and identify interchangeable parts. This preprocessing creates ready-to-use cluster data that can be quickly queried without reprocessing the detailed fitment information for each retrieval operation.
3Adaptability or versatility
If clusters of interchangeable parts are created using embedding models and similarity scores, then relevant part recommendations are improved, but this requires additional computational resources for model inference
Solution Approach 1:
The embedding model and clustering algorithm are executed in advance to create pre-computed cluster data. The system processes all part items through the embedding model, calculates similarity scores, applies constraints, and generates cluster assignments before deployment. This one-time computational investment creates a lightweight retrieval system that only needs to perform cluster identification and item filtering during actual queries.
Data Source
AI summary
Some aspects relate to technologies for performing item retrieval on a listing platform using clusters of interchangeable parts formed using fitment data. In accordance with some aspects, item data is accessed for each of a plurality of part item listings on a listing platform, where the item data for each part item listing includes fitment data. An item embedding is generated for each part item listing using the item data. The item embeddings are clustered to generate a plurality of clusters, wherein each cluster includes one or more item embeddings. Cluster data is stored for the plurality of clusters. The cluster data for each cluster associates a cluster identifier and an item listing identifier for each item embedding in the cluster. The cluster data can be leveraged to perform item retrieval for the listing platform.