File Path Vector Predictive Model for Collaboration Recommendations
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Solution Overview
Problem
Existing collaboration systems face inefficiencies in presenting relevant content object recommendations to users, often resulting in overwhelming numbers of irrelevant suggestions due to assumptions about shared interests, leading to low user engagement.
Innovation Solution
The development of techniques that utilize file path information to form predictive models for determining content object collaboration recommendations, reducing resource consumption and improving relevance by analyzing historical access activity and file path attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If collaboration recommendations are formed based on assumed shared interests between users, then the quantity of recommendations increases, but the relevance of recommendations decreases
Solution Approach 1:
The patent changes the parameters used for generating recommendations from user relationship assumptions to file path attributes and historical access patterns. By analyzing the hierarchical structure of file paths and user access behavior, the system generates recommendations based on actual usage patterns rather than assumed interests, thereby maintaining high relevance while controlling the quantity of recommendations to manageable levels.
2Quantity of substance
If the number of recommendations presented to a user increases, then more content objects are suggested, but user engagement decreases
Solution Approach 1:
The patent applies partial action by selecting only the most relevant recommendations from a larger set of potential recommendations. Instead of presenting all possible recommendations, the system uses file path analysis and historical access patterns to identify and present a focused subset of highly relevant content objects, ensuring user engagement remains high while still providing valuable recommendations.
3Measurement precision
If file path information is analyzed to form predictive models, then recommendation relevance improves, but computational resource consumption increases
Solution Approach 1:
The patent segments the file path information into hierarchical components (directories, subdirectories, files) and analyzes them separately to build predictive models. This segmentation allows the system to process file path data in manageable chunks rather than as a single large dataset, reducing computational resource consumption while maintaining high recommendation accuracy through structured analysis of path attributes.
Data Source
AI summary
Methods, systems and computer program products for recommendation systems. Embodiments commence by gathering a set of pathnames that refer to content objects of a collaboration system. A tokenizer converts at least some of the pathnames into vectors. The vectors comprise hierarchical path components such as folder names or file names, which vectors are labeled with an indication as to whether or not the folder or file referred to in a particular vector had been clicked on by one or more users. Some portion of the labeled vectors are used to train a predictive model. Collaboration recommendations may be generated that pertain to security-related recommendations.