Search Label Mapping via User Interaction Feedback
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Solution Overview
Problem
Search engines face challenges in interpreting subjective labels in search queries due to their varying meanings across different contexts, leading to difficulties in providing accurate search results that satisfy user intentions.
Innovation Solution
A computer system processes search queries by mapping subjective labels to quantifiable portions of attribute value domains, with user interactions used to customize these mappings, ensuring that subsequent search results align with user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective labels are used in search queries to describe item attributes, then users can express their search intentions more naturally, but the search engine faces difficulty in accurately interpreting these labels due to their varying meanings across different contexts
Solution Approach 1:
The system dynamically changes the interpretation parameters of labels based on user interactions and context. By monitoring user behavior patterns and adjusting the semantic mapping of labels accordingly, the system adapts the meaning of subjective labels to better match user intentions in different search contexts, thereby maintaining both ease of use and interpretation accuracy
Solution Approach 2:
The system implements feedback mechanisms by analyzing user interactions with search results (clicks, dwell time, refinements) and using this information to refine label interpretations. This continuous feedback loop allows the system to learn from user behavior and improve its understanding of subjective labels over time, resolving the contradiction between natural language input and accurate interpretation
2Device complexity
If a search engine uses fixed interpretations of labels, then the system complexity is reduced, but the search results fail to align with individual user preferences and interpretations
Solution Approach 1:
The system transitions from static, fixed label interpretations to dynamic, adaptive interpretations that evolve based on user interactions. By implementing real-time adjustments to label meanings based on user behavior patterns, the system achieves both personalization and adaptability while maintaining manageable complexity through automated learning mechanisms
Solution Approach 2:
The system employs self-service mechanisms where user interactions automatically train and refine the label interpretation model without requiring manual configuration. The system serves itself by learning from user feedback and autonomously adapting label meanings, thereby achieving user preference adaptation while keeping system complexity manageable through automated processes
Data Source
AI summary
A computer system receives a search query that includes a label corresponding to a domain of attribute values of items available for search on an online platform. In response to the search query, the computer system generates a first set of search results for output by a computing device, where the first set of search results are selected based in part on items in the first set of search results having attribute values that correspond to a first portion of the domain. The computer system maps the label to a second portion of the domain based on a user interaction with the first set of search results via the computing device. A second set of search results are generated for output by the computing device, where the second set of search results include items with attribute values that are within the second portion of the domain.