Context-Aware Auto-Completion Scoring for Search Relevance
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Solution Overview
Problem
Current auto-completion systems lack personalization and context awareness, leading to irrelevant suggestions that waste user interface space and resources, and negatively impact computer functionality.
Innovation Solution
A context-aware personalized auto-completion system that determines user context based on recent search queries and generates scores for auto-completion candidates, displaying only relevant suggestions before the user submits a search query.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If current auto-completion solutions are used, then the system provides basic word completion functionality, but the suggestions are irrelevant to the specific user and context, wasting UI space and electronic resources
Solution Approach 1:
The system pre-generates multiple possible completion suggestions and stores them before the user actually types. These pre-computed suggestions are then rapidly retrieved and filtered based on user context, eliminating the need for complex real-time computation and reducing electronic resource waste while maintaining high relevance
Solution Approach 2:
The system incorporates user feedback mechanisms where user interactions with auto-completion suggestions (selections, rejections, modifications) are fed back into the system to refine future suggestions. This continuous feedback loop improves the relevance of suggestions over time while optimizing resource usage through learned patterns
2Reliability
If context-aware personalized auto-completion is implemented, then the relevance of suggestions is improved, but the system complexity increases due to context determination and scoring mechanisms
Solution Approach 1:
The system segments the auto-completion process into distinct modular components: a context determination module that analyzes user context, a scoring module that evaluates candidate suggestions, and a selection module that chooses the best completions. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high suggestion relevance
Solution Approach 2:
The system introduces intermediary data structures such as context vectors and candidate scoring mechanisms that mediate between the raw user input and the final auto-completion suggestions. These intermediaries simplify the complex transformation process by breaking it down into manageable computational steps, reducing overall system complexity while preserving relevance
3Ease of operation
If auto-completion suggestions are displayed, then user interface functionality is enhanced, but significant UI area is consumed by irrelevant suggestions
Solution Approach 1:
The system generates a large number of potential auto-completion candidates but only displays a selective subset of the most relevant ones to the user. This partial display approach enhances UI functionality by providing helpful suggestions while minimizing the UI area consumed, as only the top-ranked candidates are shown rather than all possible completions
Data Source
AI summary
In some embodiments, a computer system detects user-entered text that has been entered in a search field of a search engine via a user interface of a computing device of a user, determines a context representation for the user-entered text based on one or more search queries submitted by the user within a particular amount of time before the user-entered text was entered, generates a corresponding score for each one of a plurality of auto-completion candidates based on the auto-completion candidate and the context representation, and causes at least a portion of the plurality of auto-completion candidates to be displayed in an auto-complete user interface element of the search field based on the corresponding scores of the at least a portion of the plurality of auto-completion candidates prior to the user-entered text being submitted by the user as part of a search query.