Search History Organization for Implicit Intent Recognition
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Solution Overview
Problem
Conventional search engines require explicit user feedback to recognize searching intent and fail to distinguish between separate tasks, leading to a poor user experience and ineffective task completion.
Innovation Solution
A system that automatically detects user searching intent by analyzing search history and navigation events, categorizes tasks, and generates collections of relevant elements to assist in task completion, allowing implicit intent recognition and collaborative feedback through social networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines require explicit user feedback (manual tagging) to recognize searching intent, then the system can identify user tasks, but the user experience deteriorates due to laborious manual operations and most users fail to tag items
Solution Approach 1:
The system automatically analyzes user navigation events, search queries, and interaction patterns to identify searching intent and create task collections without requiring manual user input. The search engine serves itself by autonomously categorizing search history and generating relevant collections based on observed user behavior patterns.
Solution Approach 2:
The system uses implicit feedback from user navigation events, click patterns, and search query sequences to infer searching intent. By analyzing these behavioral feedback signals, the system automatically identifies tasks and organizes relevant search results into contextual collections without requiring explicit user tagging.
2Device complexity
If conventional search engines aggregate all tagged items into a single list, then storage is simplified, but the system fails to distinguish between separate tasks and lose contextual information
Solution Approach 1:
The system segments search history and tagged items into distinct task-based collections rather than aggregating them into a single list. Each collection is associated with a specific inferred task or searching intent, preserving contextual information while maintaining organized data structures that reflect user goals.
Solution Approach 2:
The system adds a task context dimension to the data organization structure. Instead of a flat single-list aggregation, search results are organized in multi-dimensional collections where each collection represents a specific task context, allowing the system to distinguish between different searching intents while maintaining structural organization.
3Ease of manufacture
If search engines provide similarly formatted search results, then result delivery is standardized, but the results fail to address underlying user tasks and reduce productivity
Solution Approach 1:
The system applies different formatting and organization strategies to different collections based on their specific task contexts. Each task-based collection receives customized presentation that highlights relevant information and elements specific to that task, rather than applying uniform formatting to all search results.
Solution Approach 2:
The system dynamically adapts search result presentation based on the inferred task and user interaction patterns. Collections are automatically updated and reorganized as users interact with search results, with the formatting and organization evolving to better support the underlying task objectives.
Data Source
AI summary
Computer-storage media, methods, and systems for improving the ability of a user to accomplish a task that is pending during a search session are provided. When a user invokes the pending task, a collection of elements that are associated with the pending task are dynamically organized into a sharable content page that may be shared with a user's contacts via a social network. The user's contacts may comment on or provide feedback related to the pending task. The feedback may be published to the sharable content page and presented to the user on a user interface.