Adaptive Search Refinement Using User History and Dynamic Filters
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Solution Overview
Problem
Users often face difficulties in finding specific products on e-commerce platforms due to inadequate search terms and complex filter usage, leading to multiple searches and increased computational load.
Innovation Solution
An adaptive search recommendations system that uses machine learning to generate refined search requests and recommendations based on historical user interactions, semantic similarity, and product data, allowing users to interact with refined or new search queries through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users perform multiple searches to find specific products, then search completeness is improved, but computational load and time consumption increase
Solution Approach 1:
The system performs preliminary actions by analyzing historical search queries and user interactions before the user actually searches. It pre-generates refined search requests and recommendations based on patterns from past behavior, so that when the user searches, the system already has tailored suggestions ready to reduce the number of searches needed.
Solution Approach 2:
The system continuously collects feedback from user interactions with search results, product views, and search query modifications. This feedback is used to refine the machine learning models that generate search recommendations, creating a closed-loop system that adapts to user preferences and reduces the number of searches required over time.
2Measurement precision
If the system provides multiple refinement filters, then search precision is improved, but device complexity increases
Solution Approach 1:
The system provides self-service by automatically generating and ranking refinement filters based on the user's historical behavior and the current search context. Instead of presenting all possible filters or requiring users to manually construct complex queries, the system serves itself by using machine learning to intelligently select and present the most relevant refinement options, reducing the apparent complexity to the user.
Solution Approach 2:
The system changes parameters dynamically by adjusting the number, type, and prioritization of refinement filters based on real-time analysis of user history and search context. The machine learning model adapts the filter parameters to match individual user preferences and search patterns, providing precise results without requiring a fixed complex filter interface.
3Productivity
If the system uses machine learning to generate recommendations, then search efficiency is improved, but computational resources increase
Solution Approach 1:
The system applies partial action by selectively applying machine learning recommendation generation only when and where it adds the most value. Rather than continuously processing all possible search queries through complex ML models, the system uses simpler search logic for common queries and reserves computational resources for generating personalized recommendations based on historical patterns, achieving efficiency gains without excessive computational expenditure.
Data Source
AI summary
Examples provide improved methods for refining an initial search request of a user, as may be performed by a search recommendations system. The system may receive an initial search request via a graphical user interface and identify relevant products for the initial search request. The relevant products for the initial search request may be associated with refinement filters for refining the initial search request. The system may assign scores to the refinement filters based at least on historical search queries of the user or historical interactions of the user. The system may select refinement filter(s) based on the scores assigned to the refinement filters, and display refined search request(s) as user-interactable component(s) on the graphical user interface. The refined search request may be based on refining the initial search request using the selected refinement filter.