Query Fingerprint Analysis for Search Result Relevance
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Solution Overview
Problem
Users face challenges in accurately representing their information needs through search queries in electronic marketplaces, leading to modifications and refinements during search sessions, which can result in suboptimal search results.
Innovation Solution
A system that generates and classifies query fingerprints by analyzing user behavior, such as query modifications and actions, to modify user experiences by rearranging search results based on fingerprint information, including layouts and categories, to better match user intentions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users modify and refine their search queries during a search session, then the accuracy of representing information needs improves, but the time required for the search session increases
Solution Approach 1:
The system performs preliminary actions by analyzing user behavior patterns and generating query fingerprints in advance. When a user submits a query, the system has already prepared fingerprint information from previous similar queries, allowing it to quickly adapt and modify search results without requiring the user to spend time refining their query multiple times.
Solution Approach 2:
The system implements feedback by continuously monitoring user actions during search sessions (such as query modifications, clicks, and navigation patterns) and using this feedback to generate or update query fingerprints. This feedback loop enables the system to learn from user behavior and improve search result relevance dynamically, reducing the need for users to repeatedly modify their queries.
2Measurement precision
If the system dynamically modifies search results based on query fingerprints, then the relevance of search results improves, but the device complexity increases
Solution Approach 1:
The system introduces query fingerprints as an intermediary representation that bridges user behavior and search result modification. Instead of directly analyzing complex user behavior patterns for each search, the system uses pre-computed fingerprints as a simplified mediator that captures essential user intent and behavior characteristics, making the dynamic modification process more manageable and less complex.
Solution Approach 2:
The system changes parameters by transforming raw user behavior data into fingerprint parameters that can be efficiently stored and compared. These fingerprint parameters (such as query modification patterns, click-through rates, and navigation behaviors) serve as condensed representations that enable complex search result adaptations through relatively simple parameter matching and comparison operations.
3Adaptability or versatility
If the system analyzes user behavior to generate query fingerprints, then the personalization of search results improves, but the loss of information increases due to data processing
Solution Approach 1:
The system extracts only the most relevant and discriminative features from user behavior data to create query fingerprints. Instead of processing and storing all raw user interaction data, the system selectively extracts key patterns such as query modification sequences, preferred filtering criteria, and navigation preferences, thereby maintaining personalization capability while minimizing information loss through focused feature extraction.
Data Source
AI summary
A method and system for analyzing user behavior as users search for items within an electronic marketplace is provided. A query is submitted by a user of the electronic marketplace, the query is processed to identify a series of actions or behaviors performed by the user in relation to the query and fingerprint information for the query is determined based at least in part on analyzing the actions. In one embodiment, the electronic marketplace modifies a user experience for the user based on the fingerprint information. An interactive network region comprising search results related to a query issued by a user is generated, based on fingerprint information. Various categories of items related to a query issued by the user are identified based on fingerprint information and the search results are organized based on categories.