Category-Normalized NLP for Item-Specific Suggested Instructions
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Solution Overview
Problem
Conventional natural language processing (NLP) techniques are ineffective in generating item-specific suggested instructions for online concierge systems, often providing generic instructions across different item categories, failing to meet the unique needs of various types of items.
Innovation Solution
An online concierge system scores and normalizes candidate instruction representations using intra-category and inter-category frequency of use to generate item-specific suggested instructions, ensuring relevance to the target item category.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional NLP techniques are used to generate suggested instructions, then the system can provide suggested instructions for items, but the instructions become generic and not specific to different item categories
Solution Approach 1:
The system applies local quality by generating different suggested instructions for different item categories based on their specific characteristics. Instead of using a single generic instruction generation approach, the system tailors the instruction suggestions to the local needs of each item category (e.g., fresh produce, bakery items, meat) by analyzing category-specific instruction examples and frequency patterns.
Solution Approach 2:
The system changes parameters by dynamically adjusting the weighting between intra-category and inter-category frequencies based on the target item category. The normalization process modifies the instruction representation parameters to emphasize category-specific characteristics while reducing the impact of generic instructions, thereby generating more relevant suggestions for each category.
2Manufacturing precision
If human experts generate suggested instructions for each item, then the instructions become highly specific and relevant, but the cost and time required become incredibly expensive and time consuming
Solution Approach 1:
The system applies self-service by enabling automatic generation of suggested instructions through NLP techniques that analyze existing instruction examples. Instead of relying on human experts to manually create instructions for each item, the system autonomously processes user-provided instructions, extracts patterns, and generates suggestions automatically, thereby eliminating the need for expensive and time-consuming human intervention.
Solution Approach 2:
The system uses copying by replicating effective instruction patterns from existing instruction examples across multiple items and categories. Instead of creating unique instructions from scratch for each item, the system copies and adapts proven instruction templates and phrases from the training data, maintaining high quality while dramatically improving generation efficiency.
3Device complexity
If conventional NLP techniques provide the same suggested instructions for each item category, then the system simplifies the instruction generation process, but it fails to capture unique instructions specific to different types of items
Solution Approach 1:
The system applies local quality by generating different suggested instructions for different item categories based on their specific characteristics. Instead of using a single generic instruction generation approach, the system tailors the instruction suggestions to the local needs of each item category (e.g., fresh produce, bakery items, meat) by analyzing category-specific instruction examples and frequency patterns.
Solution Approach 2:
The system segments the instruction generation process by category, separating the analysis and generation of suggestions for different item types. This segmentation allows the system to maintain simplicity in the overall process while capturing category-specific details through dedicated frequency analysis and normalization steps for each category.
Data Source
AI summary
An online concierge system generates suggested instructions for presentation to a user. The online concierge system access instruction examples corresponding to a target item category and generates candidate instruction representations based on instruction messages within each instruction example. The online concierge system generates preliminary scores for the candidate instruction representations that are directly related to an intra-category frequency of use of the instruction tokens of the candidate instruction representation within the target item category. The online system normalizes these preliminary scores for the candidate instruction representations based on the inter-category frequency of use of the instruction tokens in all item categories to generate final scores for the candidate instruction representations. The online concierge system selects a set of instruction representations based on these final scores and generates suggested instructions based on the set of instruction representations.