Affinity-Based Information Filtering for Personalized Search Narrowing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current intranet services fail to automatically identify and present relevant business information to employees, requiring them to seek out information independently and often missing important content.
Innovation Solution
A system that learns employee interests and identifies relevant information by using user-defined and computed properties to narrow down search results, allowing users to select properties for refining search sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If intranet services present all available information to users, then information completeness is improved, but information overload and user difficulty in finding relevant content worsens
Solution Approach 1:
The patent segments information by organizing it into hierarchical categories and tags, allowing the system to present comprehensive information while enabling users to navigate through structured segments to find relevant content efficiently
Solution Approach 2:
The system changes the parameter of information presentation by dynamically adjusting what information is displayed based on user profile, preferences, and interaction history, transforming the static complete information set into a dynamic personalized view
2Measurement precision
If users manually search for information themselves, then information precision is improved, but time consumption and productivity worsens
Solution Approach 1:
The system performs self-service by automatically analyzing user behavior, computing affinities, and curating personalized information feeds without requiring manual user effort, thereby maintaining high information relevance while dramatically improving retrieval efficiency
Solution Approach 2:
The system implements feedback loops where user interactions with information are continuously monitored and used to refine affinity computations, improving both information precision and retrieval efficiency over time through automated learning
3Measurement precision
If the system learns and personalizes information for each user, then information relevance is improved, but system complexity worsens
Solution Approach 1:
The patent implements a universal affinity computation framework that handles multiple types of data (user profiles, content metadata, interaction logs) through a single unified algorithmic approach, reducing system complexity while maintaining high information relevance
Data Source
AI summary
The disclosed technology provides systems and methods for filtering information based on a set of properties. The information consists of a set of items that the user is interacting with, such as documents, presentations, audio and video files, and the like. The properties can be specified by the user (by, for example, putting a set of items in lists and folders), based on actions taken by users in the system (such as commenting on, or liking, or viewing an item), or can represent a variety of other characteristics. Related properties can also be grouped together. Furthermore, the disclosed techniques provide mechanisms for automatically identifying useful properties and providing an indication of those useful properties to a user to use in narrowing results.