Source Similarity Scoring for Online Concierge Replacement Suggestions
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Solution Overview
Problem
Current online concierge systems struggle to effectively suggest replacement items when an ordered item is unavailable, as they fail to balance user preferences for similar items from the same source with the need to account for differences in item source.
Innovation Solution
The online concierge system uses a scoring system to suggest replacement items, combining a replacement score indicating the likelihood of user acceptance with a source similarity score that prefers items from the same source as the ordered item, while allowing for but penalizing items from different sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the online concierge system suggests replacement items from the same source as the ordered item, then user satisfaction and brand loyalty are improved, but the system loses opportunities to promote diverse sources and competitor products
Solution Approach 1:
The system changes the parameter of source similarity by introducing a configurable threshold parameter. When the source similarity score exceeds the threshold, items from the same source are preferred; when it falls below, items from different sources are considered. This parameter-based approach allows dynamic adjustment between maintaining brand loyalty and promoting diversity based on business needs.
Solution Approach 2:
The system creates a composite scoring mechanism that combines multiple factors: source similarity score, replacement score, and advertisement bid. This composite approach integrates competing objectives (brand loyalty vs. diversity vs. revenue) into a unified ranking system, where the final recommendation balances all three considerations rather than prioritizing one exclusively.
2Reliability
If the online concierge system prioritizes items from the same source, then brand loyalty is maintained, but the complexity of the recommendation system increases due to multiple scoring factors
Solution Approach 1:
The system segments the recommendation process into distinct modular components: source identification module, source similarity scoring module, replacement scoring module, and advertisement bid integration module. Each module handles a specific aspect independently, making the overall complex system manageable through clear separation of concerns and independent optimization of each segment.
Solution Approach 2:
The system introduces an intermediary scoring mechanism that mediates between source preference and replacement quality. The source similarity score acts as an intermediary factor that modifies but does not dominate the final recommendation, allowing the system to balance brand loyalty with actual replacement suitability through a layered evaluation approach.
3Speed
If the system suggests replacement items without considering source similarity, then processing speed is improved, but user acceptance of replacement items decreases
Solution Approach 1:
The system performs preliminary action by pre-computing and storing source embeddings for all items in the catalog before replacement recommendations are needed. These embeddings capture source characteristics in advance, allowing the system to quickly calculate source similarity scores during actual replacement scenarios without performing complex computations at recommendation time, thus maintaining speed while improving acceptance.
Data Source
AI summary
An online concierge system suggests replacement items when an ordered item may be unavailable. To promote similarity of sources between the replacement item with the ordered item, candidate replacement items are scored, in part, based on a source similarity score based on a source of the candidate replacement item and a source of the ordered item. The source similarity score may be determined by a computer model based on user interactions with item sources. The similarity score may be based on source embeddings that may be determined based on respective item embeddings or may be determined by training source embeddings directly from user-source interactions. The similarity score for a candidate replacement item may be combined with a replacement score indicating the user's likelihood of selecting the candidate replacement item as a replacement to yield a total score for selection as suggestion as a replacement for the ordered item.