Relevance-Based Content Recommendation for Collaborative Repositories
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Solution Overview
Problem
Users face inefficiencies in adding content to collaborative repositories, requiring manual searching and multiple steps to locate and copy or move files, especially when creating new content or using existing content created by others.
Innovation Solution
A relevance-based system that selects and recommends URI-addressable objects based on predefined criteria, such as user interactions, popularity, and metadata, to simplify the process of adding content to collaborative repositories, reducing the need for manual searching and streamlining the content addition process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually search for content by clicking through folders and using copy-move operations, then they can locate and add content to the collaborative repository, but the process requires multiple steps and consumes significant time
Solution Approach 1:
The system pre-processes and analyzes user data, interactions, and repository contents before the user initiates content creation. By preparing relevant object recommendations in advance based on user behavior patterns and repository context, the system eliminates the need for manual folder navigation and searching, directly presenting potential content options to the user.
Solution Approach 2:
The system autonomously identifies and recommends relevant objects for content creation without requiring active user searching. The automated recommendation engine analyzes user interactions, popularity metrics, and metadata to self-select appropriate content candidates, allowing the system to serve itself in the content discovery process rather than relying on manual user exploration.
2Ease of operation
If the system provides automated relevant object recommendations, then user efficiency is enhanced and time is reduced, but system complexity increases due to the need for relevance criteria evaluation
Solution Approach 1:
The relevance evaluation system is divided into distinct modular components: data collection module, criteria evaluation module, ranking module, and recommendation module. Each module handles a specific aspect of the recommendation process, allowing independent optimization and maintenance. This segmentation manages system complexity by breaking down the complex relevance determination into manageable, specialized subsystems.
Solution Approach 2:
The recommendation system employs universal relevance criteria that can evaluate multiple types of objects (documents, folders, links) through a common framework. The same relevance criteria and evaluation mechanisms work across different object types and user contexts, reducing the need for separate specialized systems and thereby managing complexity while maintaining versatility.
Data Source
AI summary
Techniques are described herein that are capable of using relevant objects to add content to a collaborative repository. The relevant objects are selected from URI-addressable objects based on each relevant object satisfying one or more relevance criteria. The relevant objects are recommended via a user interface. A determination is made that a specified relevant object is selected from the relevant objects. In one example, the content may be added to (e.g., created in) the collaborative repository based on the specified relevant object. In another example, the specified relevant object may be used to represent a new object type in a new content menu. For instance, the new object type may be selectable to cause an object of the new object type to be created in the collaborative repository.