Aggregating Action Trails for Task-Based Resource Ranking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in discovering relevant information for their tasks as existing web browsers and search systems do not effectively partition user histories by tasks, leading to the need for manual browsing and multiple queries to find relevant resources, especially when there is no preexisting ontology for activity classification.
Innovation Solution
A method that aggregates action trail data from multiple users to cluster tasks and rank resources based on similarity, providing task-relevant resources to users even when they do not explicitly query for them, using a 'crowdsourcing' technique that combines data for related topics and identifies relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If chronological web histories are provided to users, then users can recall resources they found informative, but the histories do not partition by task requiring manual browsing through all user actions in order
Solution Approach 1:
The patent segments chronological web histories into task-specific action trail clusters. Each cluster groups user actions (queries, resource selections, rejections) that relate to a particular task, allowing users to directly access task-relevant resources without browsing entire chronological histories. This segmentation transforms the flat chronological structure into organized task-based groups, resolving the contradiction between information recall and time efficiency.
2Reliability
If users manually browse and issue multiple queries to find relevant resources for a task, then comprehensive search coverage is achieved, but the process is time-consuming and inefficient
Solution Approach 1:
The patent performs preliminary clustering of user actions into task-based action trails before users need to search. By pre-organizing queries, resource selections, and rejections into coherent task clusters, the system prepares task-relevant resources in advance. When users initiate a search, the system can immediately present pre-organized task-specific results rather than requiring users to manually browse and query through all historical data, thus maintaining completeness while reducing time.
3Adaptability or versatility
If action trail data from multiple users is aggregated and clustered by task, then task-relevant resources can be identified and ranked, but the system complexity increases
Solution Approach 1:
The patent implements self-service clustering where the system automatically analyzes and clusters action trail data from multiple users based on task patterns without requiring manual intervention. The clustering algorithm autonomously groups user actions into task-specific clusters by identifying patterns in queries, resource selections, and rejections. This self-service approach enables the system to handle increased data complexity and provide adaptive task-based recommendations while minimizing the need for complex manual configuration or intervention.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for aggregating task data for multiple users. In one aspect, a method includes accessing action trail data that corresponds to a task and resources related to that task, wherein each task relates to one or more related topics and is defined by a sequence of user actions corresponding to the resources related to that task; clustering the action trails based on the action trail data such that each action trail cluster corresponds to a particular task and includes the action trails corresponding to that particular task; and for each action trail cluster, ranking the resources that correspond to the included action trails according to the topics of the particular task.