Semantic Tree Content Recommendation System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional media recommendation systems suffer from narrow and inflexible recommendation scopes, failing to provide users with surprising and inspiring content discoveries, as they typically rely on similarities between content items or user preferences.
Innovation Solution
A machine learning-based method that processes metadata queries to determine semantic flow knowledge by forming a semantic tree representation, allowing for the measurement of semantic distance between tags and providing recommendations through semantic paths that span contrasting concepts, thereby offering diverse and relevant content suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional recommendation systems use predefined similarity dictionaries or collaborative filtering based on user behavior, then they can provide personalized recommendations, but the recommendation scope becomes narrow and inflexible
Solution Approach 1:
The patent changes the fundamental parameter of tag relationship from static predefined similarity to dynamic semantic flow. Instead of using fixed similarity dictionaries, the system learns semantic relationships between tags through machine learning, allowing the recommendation scope to expand beyond traditional similarity boundaries while maintaining personalization capabilities
Solution Approach 2:
The patent introduces semantic flow as an intermediary concept that bridges the gap between user preferences and content recommendations. The semantic flow model acts as a mediator that translates user behavior patterns into meaningful semantic paths, enabling broader content discovery while preserving the personalization function
2Reliability
If recommendation systems focus on content similarity to user preferences, then they provide relevant recommendations, but they fail to provide surprising and inspiring content discoveries
Solution Approach 1:
The patent applies dynamics by making the recommendation process adaptive and flexible through semantic flow. The system can dynamically adjust the balance between relevance and surprise by controlling the semantic distance traversal in the learned tag space, allowing recommendations to evolve from similar content to increasingly diverse content based on user interaction
Solution Approach 2:
The patent adds a new dimension to recommendation by introducing semantic flow paths that traverse multiple semantic layers. Instead of flat similarity matching, the system creates multi-dimensional semantic paths that connect user preferences to surprising content through intermediate semantic concepts, enabling both relevance and discovery
3Adaptability or versatility
If systems use semantic tree representation with learned inter-connections, then they can measure semantic distance and provide diverse recommendations, but the computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-learning the semantic flow model and constructing the semantic tree structure in advance. This offline training phase captures the semantic relationships between tags, so that during online recommendation, the system only needs to traverse the pre-built semantic paths rather than computing relationships in real-time, significantly reducing computational complexity during deployment
Data Source
AI summary
Systems and methods for recommending content in a media system are described. In an embodiment, the method identifies a reference metadata tag associated with content in a database, retrieves a data structure storing information of learned associations between the reference metadata tag and a plurality of other metadata tags, the data structure defining a tree of nodes each associated with a corresponding metadata tag, a root node of the retrieved tree associated with the identified reference metadata tag. A path is extracted from the data structure, spanning from the reference metadata tag to a target metadata tag, the extracted path identifying a sequence of metadata tags defined by nodes along the extracted path. Recommended content items are returned based on the sequence of metadata tags identified by the extracted path.