Reconciling Catalog Item Attributes Using Feedback Data
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Solution Overview
Problem
In electronic commerce systems with multiple merchants, discrepancies in product descriptions can lead to inaccurate item attributes, resulting in users receiving items different from what they ordered.
Innovation Solution
A merchant contribution reconciliation application determines an authoritative attribute by processing contributions from multiple merchants, incorporating feedback data such as sales data to balance the improvement of attributes and the risk of changing the item identity, thereby ensuring accurate descriptions in the electronic catalog.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple merchants provide product descriptions independently, then each merchant can offer their own product information, but discrepancies and inaccuracies in item attributes occur
Solution Approach 1:
The system incorporates feedback data including sales data, return data, and customer service data to continuously improve attribute accuracy. Sales data indicates which attributes lead to successful purchases, return data reveals attribute mismatches between description and actual item, and customer service data highlights common customer queries and complaints related to attribute accuracy. This feedback loop enables the system to learn from real-world outcomes and refine attribute synthesis.
Solution Approach 2:
The system introduces an intermediary synthesis process that mediates between multiple merchant contributions and the final catalog attribute. Rather than directly using any single merchant's description, the system acts as an intermediary that collects, compares, validates, and synthesizes attributes from multiple sources, incorporating feedback data to resolve discrepancies and produce an authoritative attribute that represents the consensus and accuracy across all contributions.
2Measurement precision
If authoritative attributes are synthesized from multiple merchant contributions, then attribute accuracy improves, but the risk of changing item identity increases
Solution Approach 1:
The system performs preliminary validation and comparison of merchant contributions before final synthesis. It pre-identifies critical attributes that define item identity and ensures these remain consistent across all merchant submissions. By preparing and validating attributes in advance, the system reduces the risk of unintended identity changes while still improving accuracy through synthesis.
Solution Approach 2:
The system implements safeguard mechanisms that cushion against erroneous attribute changes. It maintains version history, performs consistency checks against known item identities, and uses feedback data to predict potential identity conflicts before they occur. These protective measures allow the system to synthesize attributes aggressively while maintaining a safety net that prevents harmful identity changes.
3Measurement precision
If feedback data is incorporated to improve attribute accuracy, then item description quality increases, but system complexity increases
Solution Approach 1:
The system segments the feedback processing into distinct, independent modules: sales data processing, return data processing, and customer service data processing. Each module handles a specific type of feedback independently, then their results are integrated in the synthesis process. This segmentation reduces complexity by making each processing step manageable and independent, while still achieving comprehensive feedback incorporation.
Data Source
AI summary
Disclosed are various embodiments for using contributor-provided attributes in conjunction with an electronic item catalog. A computer reconciles a plurality of attributes for an item in an electronic catalog using feedback data related to the item. Each of the attributes is provided by one of a plurality of merchants. The reconciled attribute of the item is updated in the electronic catalog.


