Projection Mining for Recommendation Systems Without History
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Solution Overview
Problem
Conventional recommendation systems struggle with recommending products or sessions without a history, as they rely on specific historical data, limiting their applicability to unique or single-use items where direct proxy mapping is not feasible.
Innovation Solution
Projection Mining provides a unified framework that maps objects into attributes and then into abstract meta-attributes, enabling the establishment of relationships between objects without relying on specific histories, using methods like linear algebra and matrix inversions to generate new data objects and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation systems use historical data to generate recommendations, then recommendation accuracy is improved, but adaptability to unique or single-use items deteriorates
Solution Approach 1:
The patent transforms the data representation from a direct object-space approach to an attribute-space approach by projecting objects into attributes and then into meta-attributes. This dimensional transformation enables the system to handle unique items without historical data by operating in a higher-dimensional abstract space where semantic relationships can be established between objects based on their attributes rather than their historical behavior.
Solution Approach 2:
The patent introduces attributes and meta-attributes as intermediary representations between objects and their historical data. These intermediaries serve as a bridge that allows the system to infer relationships between unique items and historical data indirectly, enabling recommendations for items without direct historical records by mediating through their attribute spaces.
2Measurement precision
If recommendation systems rely on specific historical data, then predictive accuracy is improved, but generalizability to new situations deteriorates
Solution Approach 1:
The patent creates a universal framework where the same projection and learning mechanisms can handle both historical data and new situations. The attribute and meta-attribute spaces serve as a universal representation that can accommodate diverse data types and scenarios, allowing the system to generalize from historical patterns to new situations without requiring scenario-specific models.
Solution Approach 2:
By moving to a higher-dimensional meta-attribute space, the system achieves greater generalizability. This dimensional elevation allows abstract semantic relationships to be captured that transcend specific historical instances, enabling the model to apply learned relationships to new situations while maintaining predictive accuracy through the structured projection framework.
3Productivity
If conventional data mining uses clustering or prediction models, then processing efficiency is improved, but interpretability and transparency deteriorate
Solution Approach 1:
The patent segments the data processing into distinct conceptual layers: objects, attributes, and meta-attributes. This segmentation makes the model more interpretable by breaking down the complex relationships into manageable components that can be individually analyzed and explained, while maintaining processing efficiency through the structured projection framework.
Solution Approach 2:
The patent changes the representation parameters from direct object-based models to attribute-based projections. This parameter transformation maintains computational efficiency while significantly improving interpretability, as the meta-attribute space provides a more transparent representation of relationships that can be more easily explained and conceptualized by users.
Data Source
AI summary
A method for projection mining comprises performing a first projection on a first data object of a first type comprising a plurality of data entries and a second data object of a second type comprising a plurality of data entries to create definitions of attributes of the first data object and definitions of attributes of the second data object, performing a second projection of the definitions of the attributes of the first data object and the definitions of the attributes of the second data object into a space of meta-attributes based on semantic relationships among the attributes of the first data object and the second data object, learning relationships between the space of meta-attributes formed by the projections of the first data object and the second data object and a space of meta-attributes relating to new data not included in the first data object and the second data object, and generating at least one new data object of the first or second type based on the new data using the learned relationships.