Partial Search Ranking via Implicit Interaction Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing online systems face challenges in accurately ranking partial search query results due to limited user interaction data, especially when users consume search results without explicit clicks, leading to reduced feedback and less effective search relevance.
Innovation Solution
The system monitors and utilizes both explicit and implicit user interactions, such as cursor hovering and reading summaries, to determine relevance scores for candidate objects, adjusting feature weights to rank partial search query results efficiently and promptly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system relies on explicit result clicks to determine search relevance, then the search relevance signal is clear and strong, but the coverage of user interactions is limited and feedback volume is reduced
Solution Approach 1:
The system implements feedback by monitoring implicit user interactions (cursor hovers, time spent on results) and using this feedback to adjust search result rankings. This creates a continuous loop where user behavior automatically refines search relevance without requiring explicit clicks, thereby increasing both the precision of relevance signals and the volume of feedback data.
Solution Approach 2:
The patent introduces implicit interaction signals as an intermediary between traditional explicit clicks and search relevance determination. These intermediate signals (such as cursor positioning and dwell time) provide additional nuanced information about user intent, enabling the system to capture feedback that would otherwise be lost without requiring explicit user actions.
2Ease of operation
If improved search result summaries are provided to satisfy user information needs, then user information needs are met without clicks, but the search click data volume is reduced
Solution Approach 1:
The system captures implicit feedback signals (cursor hovers, time spent viewing summaries) that occur when users interact with improved search result summaries. This allows the system to maintain enhanced summaries that satisfy user information needs while simultaneously collecting rich feedback data from these implicit interactions, converting what would be lost click data into alternative valuable signals.
3Measurement precision
If the system monitors and utilizes implicit user interactions to rank search results, then the relevance accuracy of search results is improved, but the system complexity increases
Solution Approach 1:
The system implements multi-functionality by using the same monitoring infrastructure to serve multiple purposes: tracking user interactions for relevance ranking, analyzing behavior patterns for search optimization, and capturing implicit feedback signals. This universal approach allows the system to improve relevance accuracy through implicit interaction monitoring without proportionally increasing complexity, as one system performs multiple functions.
Data Source
AI summary
A client device receives search queries and displays via a user interface, search results representing a set of the records based on the search queries. The client device monitors implicit user interactions with search query terms and records displayed in response to various search queries, for example, implicit user interactions representing movements of cursor on the portion of user interface displaying a particular record. The client device receives a partial search query. Partial search results representing a set of the records based on the partial search query are determined for display via the user interface. The relevance score for each record is determined based on implicit user interactions associated with past search queries. The partial search results are ranked based on the relevance scores and displayed by the client device according to the ranked order.