Personalized Autocomplete Scoring With Session and Cluster Matching
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Solution Overview
Problem
Existing autocomplete systems lack the ability to provide personalized and efficient autocomplete predictions, leading to increased operational load and reduced search accuracy, particularly in specialized domains like healthcare where search terms are hard to spell and highly specialized.
Innovation Solution
Utilizes a combination of general and individual curated search corpora, along with machine learning models to generate session-agnostic and session-aware autocomplete scores, incorporating edit distance, trie-based frequency, and per-cluster matching to provide personalized autocomplete suggestions based on user-specific data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional autocomplete systems are used, then the system structure is simple, but search accuracy is reduced and repeated search operations are needed
Solution Approach 1:
The search corpus is segmented into multiple clusters based on topic relevance, and the autocomplete scoring is segmented into multiple independent components (session-agnostic score, session-aware score, per-cluster match score). This segmentation allows each component to specialize in specific aspects of relevance evaluation, improving overall search accuracy without requiring a monolithic complex system.
Solution Approach 2:
The autocomplete prediction system uses nested evaluation structures where session-agnostic scores are calculated for all candidates, then session-aware scores are applied as refinements, and finally per-cluster match scores are applied as additional filtering. This nested approach layers multiple levels of refinement to achieve high search accuracy while maintaining manageable system complexity through modular architecture.
2Measurement precision
If personalized autocomplete predictions are implemented, then search accuracy improves, but computational load and processing time increase
Solution Approach 1:
Session-agnostic autocomplete scores are pre-calculated for all candidate search results before session-aware filtering is applied. This preliminary action allows the system to establish baseline relevance scores efficiently, then apply more computationally intensive session-aware and per-cluster match scoring only to promising candidates, thereby improving computational efficiency while maintaining personalized accuracy.
Solution Approach 2:
The system applies session-aware and per-cluster match scoring not to all candidates uniformly, but selectively to candidates that pass preliminary filters. This partial application of computationally intensive personalized scoring to only relevant candidates maintains high search accuracy while significantly reducing overall computational load compared to applying all scoring mechanisms to every candidate.
3Reliability
If multiple scoring mechanisms are used for personalized predictions, then autocomplete quality improves, but processing time increases
Solution Approach 1:
The scoring process is segmented into three distinct phases: session-agnostic scoring (fast, baseline relevance), session-aware scoring (moderate complexity, user context refinement), and per-cluster match scoring (targeted, topic-specific refinement). This segmentation allows the system to apply increasing levels of complexity progressively, improving autocomplete quality while minimizing total processing time by avoiding unnecessary computation on irrelevant candidates.
Solution Approach 2:
Session-agnostic scores serve as a preliminary filter that quickly identifies potentially relevant candidates before more time-consuming personalized scoring is applied. This preliminary action ensures that computationally intensive session-aware and per-cluster match scoring are only performed on candidates with baseline relevance, thereby improving autocomplete quality through multiple scoring mechanisms while controlling processing time.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing personalized autocomplete predictions. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform personalized autocomplete predictions using a general search corpus and/or individual curated search corpus.