Autocomplete Search Weight Adjustment for Periodic Events
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Solution Overview
Problem
Existing autocomplete systems in search engines fail to respond effectively to periodic events and temporal shifts in user search patterns, often providing biased results after the event has peaked due to lag in incorporating recent data.
Innovation Solution
The system captures and utilizes information about periodic events, such as holidays or sporting events, to adjust autocomplete results in real-time by associating partial user inputs with relevant categories, weighting suggestions based on historical data and temporal models to anticipate and reflect current interest spikes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system uses traditional autocomplete methods based on historical search data, then it maintains system simplicity and data consistency, but it fails to respond timely to periodic events and temporal shifts in user search patterns
Solution Approach 1:
The system performs preliminary analysis of search queries to identify periodic events and temporal patterns in advance. By detecting events like holidays or sporting events before they peak in search volume, the system can proactively adjust autocomplete weights to anticipate user search patterns during these events, eliminating response lag.
Solution Approach 2:
The system continuously monitors search query patterns and uses this feedback to dynamically adjust autocomplete suggestions. By analyzing real-time search data and comparing it against historical patterns, the system detects temporal shifts and periodic events, then adjusts weights accordingly to maintain high relevance without lag.
2Reliability
If the system dynamically adjusts autocomplete weights based on temporal models and periodic events, then it improves autocomplete relevance and user satisfaction, but it increases system complexity
Solution Approach 1:
The system segments the autocomplete adjustment process into distinct components: event detection module, temporal pattern analysis module, weight calculation module, and suggestion generation module. Each component handles a specific aspect of the problem, making the overall complex system manageable and maintainable while achieving high autocomplete accuracy.
Solution Approach 2:
The system changes parameters such as autocomplete weights and time window durations based on detected temporal patterns and periodic events. By adjusting these parameters dynamically rather than restructuring the entire system, the patent achieves high accuracy while minimizing the increase in system complexity.
3Productivity
If the system incorporates real-time temporal data and periodic event detection, then it provides timely and relevant autocomplete results, but it increases computational resource requirements
Solution Approach 1:
The system applies partial adjustment to autocomplete weights based on the significance and stage of detected periodic events. Rather than continuously adjusting all parameters at full intensity, the system modulates the degree of adjustment according to event importance and current search patterns, reducing computational energy while maintaining high search accuracy.
Data Source
AI summary
Embodiments describe systems and methods for identifying temporal demand for queries and using metadata to modify autocomplete results. In one embodiment, a record of historical queries is stored by a system and analyzed to identify periodic or repeated events where demand for autocomplete results associated with one or more categories deviates from normal demand. A temporal model based on this record is used to adjust autocomplete search results during subsequent time periods associated with the repeated events.