Attribute Vector Aggregation for Recommendation Systems
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Solution Overview
Problem
Existing recommendation systems face challenges in accurately accounting for user preferences nuances and synthesizing recommendations for higher-level entities like events and venues, particularly due to the 'cold start' problem and limitations in computational efficiency.
Innovation Solution
The implementation of attribute vectors to combine and synthesize media, artist, event, and venue attributes, allowing for weighting of both objective and subjective factors, enabling the generation of complex recommendations through aggregation and weighting processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing recommendation systems are used to generate recommendations, then recommendations can be provided to users, but the systems fail to accurately account for user preferences nuances and struggle with synthesizing recommendations for higher-level entities like events and venues
Solution Approach 1:
The patent segments user preferences and entity attributes into discrete attribute vectors with specific dimensions. Each user and entity (media, artist, event, venue) is represented by a vector of weighted attributes, allowing precise measurement and comparison while maintaining adaptability across different entity types and preference nuances.
2Measurement precision
If complex attribute vectors are used to capture user preferences nuances, then recommendation accuracy improves, but computational complexity increases
Solution Approach 1:
The patent changes the parameter representation from traditional collaborative filtering matrices to attribute vectors with weighted dimensions. This allows accurate capture of user preferences through parameter weighting while simplifying the computational process through vector operations and aggregation functions that are more efficient than matrix factorization.
3Ease of manufacture
If traditional recommendation methods are used, then implementation is simpler, but the cold start problem persists and computational efficiency is limited
Solution Approach 1:
The patent applies preliminary action by pre-defining attribute vectors for all entities (users, media, artists, events, venues) with their inherent attributes before recommendations are needed. This allows the system to immediately generate recommendations for new users or entities without requiring historical interaction data, effectively solving the cold start problem while maintaining implementation simplicity.
Data Source
AI summary
Methods and apparatus are disclosed to generate a recommendation, including an attribute vector aggregator to form a resultant attribute vector based on an input set of attribute vectors, the set of attribute vectors containing at least one of a media attribute vector, an attendee attribute vector, an artist attribute vector, an event attribute vector, or a venue attribute vector, and a recommendation generator, the recommendation generator including: a vector comparator to perform a comparison between an input attribute vector and other attribute vectors and a recommendation compiler to create one or more recommendations of at least one of media, an artist, an event, or a venue based on the comparison.