Attribute Profiling for Collaborative Filtering
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Solution Overview
Problem
Existing collaborative filtering methods based on genre and tags are too general and arbitrary, failing to accurately recommend products to users as they do not effectively capture the specific reasons why users like or dislike products, leading to irrelevant recommendations and potential loss of business.
Innovation Solution
A system and method that generates product and user profiles using attribute profiling, where users assign value data to attributes representing reasons for liking or disliking content-based products, allowing for accurate matching and recommendation of products that align with user preferences, incorporating bio data and meta-data analysis to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If collaborative filtering is based on genre categorization, then the system can easily categorize products, but the recommendations become too general and fail to capture specific user preferences
Solution Approach 1:
The patent segments the genre category into multiple hierarchical levels: genre level, sub-genre level, and attribute level. Each level provides more specific granularity, allowing the system to capture user preferences at different depths of specificity while maintaining the organizational structure of categorization.
Solution Approach 2:
The patent introduces a new dimension of analysis by moving from traditional genre-based categorization to attribute-based profiling. Instead of relying solely on genre labels, the system analyzes products and users based on multiple attributes (e.g., plot characteristics, character types, themes) to create detailed profiles that capture nuanced preferences.
2Adaptability or versatility
If users create random tags to describe products, then users can express their preferences freely, but the tags lack structure and cannot be effectively linked together
Solution Approach 1:
The patent segments the tag system into structured attribute categories. Instead of allowing completely free-form tags, the system organizes user inputs into predefined attribute dimensions (plot, characters, themes, etc.), maintaining user freedom while imposing structure for effective linking and analysis.
Solution Approach 2:
The patent introduces attribute profiles as an intermediary layer between user tags and product recommendations. This intermediary structure processes and organizes the tags, creating meaningful connections and relationships that enable effective recommendation without requiring direct complex tag-linkage logic.
3Device complexity
If the recommendation system uses general genre ratings, then the system remains simple to implement, but the recommendations fail to be relevant to user interests and needs
Solution Approach 1:
The patent segments the recommendation process into multiple stages: collecting user feedback, generating attribute profiles at multiple levels, matching products to profiles, and generating recommendations. This segmentation allows the system to handle complexity systematically while maintaining clarity in implementation.
Solution Approach 2:
The patent implements a dynamic recommendation approach where attribute profiles are continuously updated based on new user feedback and product data. The system adapts to changing user preferences and incorporates new information automatically, improving recommendation relevance over time without requiring complex manual reconfiguration.
Data Source
AI summary
A system and/or a method of collaborative filtering based on attribute profiling is disclosed. In one embodiment, a method includes generating a product profile of a content-based product through applying any number of value data assigned by a user to a set of attributes embodying possible reasons as to why the user reacts to the content-based product, generating a user profile of a user through applying a group of value data assigned by the user to a content-based product sharing the set of attributes and recommending a different content-based product to the user when a different product profile of the different content-based product matches with the user profile beyond a threshold value. The method may include recommending the different content-based product to a different user when a different user profile of the different user matches with the user profile of the user who has subscribed to the different content-based product.