E-commerce Query Suggestion System Using Inventory-Aware Ranking
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Solution Overview
Problem
E-commerce query suggestion systems face challenges in providing relevant suggestions due to transient inventory, long tail queries, and noisy data, which can lead to poor user experience and monetary losses, as they need to balance coverage and precision while handling frequent and rare queries effectively.
Innovation Solution
A system that includes a query detection, classification, and ranking subsystem to analyze query logs, utilize distributed platforms like Hadoop for parallel processing, and employ cleaning techniques to remove noise, ensuring that suggestions are relevant and available, even for rare queries, by using a combination of popularity and purchase-efficiency scores, and semantic similarity measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If query suggestion systems use traditional web search methods, then they can provide coverage for various queries, but the precision and relevance of suggestions deteriorate due to transient inventory and e-commerce specific challenges
Solution Approach 1:
The system applies different scoring mechanisms and data sources to different query types. Popular queries use aggregated statistics while long-tail queries use session-based co-occurrence. Inventory availability and purchase efficiency are weighted differently based on query characteristics, ensuring each query receives tailored suggestions appropriate to its specific context and challenges.
Solution Approach 2:
The system dynamically adjusts parameters such as query frequency thresholds, inventory weightings, and time window sizes based on query popularity and characteristics. This allows the system to adapt the suggestion generation process to handle both high-frequency and low-frequency queries effectively, maintaining precision across varying coverage requirements.
2Measurement precision
If the system focuses on popular queries, then precision is improved, but recall for long tail queries deteriorates
Solution Approach 1:
The system segments queries into popular and long-tail categories based on frequency thresholds. Different algorithms and data sources are applied to each segment: aggregated statistics for popular queries and session-based co-occurrence for long-tail queries. This segmentation enables the system to maintain high precision for popular queries while improving recall for rare queries without compromise.
Solution Approach 2:
For long-tail queries where data is scarce, the system applies session-based co-occurrence from user sessions even when the statistical significance is limited. This partial application of session-based methods complements the aggregated statistics approach, providing additional coverage for rare queries while maintaining overall system precision through the combination of both methods.
3Quantity of substance
If the system includes more query suggestions to improve coverage, then the likelihood of suggestions leading to no inventory issues increases
Solution Approach 1:
The system performs preliminary filtering of query suggestions based on current inventory availability before presenting them to users. By checking inventory status in advance and excluding out-of-stock items from suggestion lists, the system maintains high coverage of useful queries while preventing users from encountering no-inventory situations, thus improving reliability.
Solution Approach 2:
The system incorporates real-time inventory feedback into the query suggestion generation process. Suggestions are dynamically adjusted based on current stock levels, and the system learns from user interactions with suggestions to improve future inventory-aware recommendation accuracy, balancing coverage and reliability.
4Quantity of substance
If the system uses session-based co-occurrence for all queries, then recall is improved, but precision deteriorates due to noisy data
Solution Approach 1:
The system dynamically selects between aggregated statistics and session-based co-occurrence methods based on query characteristics and data quality indicators. For queries with sufficient historical data, aggregated statistics provide stable precision. For queries where session-based data adds value without excessive noise, it is incorporated to improve recall, creating a flexible, adaptive system.
Solution Approach 2:
The system extracts and applies session-based co-occurrence information selectively, removing noisy or low-confidence associations from the suggestion generation process. By filtering session data through quality thresholds and confidence measures, the system captures useful long-tail patterns while eliminating detrimental noise.
Data Source
AI summary
Providing query suggestions using a query log including a number of user sessions that comprise training data including a sequence of a plurality of sets of queries. Some of the sets of queries include query transitions followed by a purchase related event. The query log is cleaned and normalized. Query log stationary scores and transition scores of at least some of the plurality of sets is generated. A set of query suggestions is built and similarity scores are computed for at least some of the set of query suggestions to determine whether individual ones of the at least some of the set of query suggestions meet a predetermined assurance level. Those that meet the level are included as elements of the set of query suggestions that meet the predetermined assurance level. That set of query suggestions are mixed and ranked in accordance with a user behavior sought to be optimized.