Personalized Search Engine Using Collaborative Metric Learning
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Solution Overview
Problem
Existing search and discovery systems face limitations in providing personalized recommendations, especially for new users or new content, as they rely on large datasets and user history, struggling with 'cold start' issues and inability to handle time-critical content effectively.
Innovation Solution
A content-centric personalized recommendation system using collaborative metric learning (CML) that operates with small data amounts, enabling real-time personalization by mapping user and content embeddings into a shared vector space, allowing for immediate incorporation of new data and users without prior training, and providing individualized recommendations across various content types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing search and discovery systems use large datasets and user history for personalization, then recommendation accuracy is improved, but the system cannot handle new users or new content effectively (cold start problem)
Solution Approach 1:
The system performs preliminary actions by pre-processing and embedding content features into a unified vector space before they are needed for recommendations. Content embeddings are created in advance using content-only information, so when new users or new content arrive, the system can immediately perform similarity comparisons without requiring historical interaction data. This preliminary embedding of content features resolves the cold start problem by having the infrastructure ready to handle new entities immediately.
Solution Approach 2:
The patent introduces content embeddings as an intermediary representation that bridges the gap between content and user preferences. Instead of directly comparing new users or new content with historical data, the system uses content embeddings as a mediator that can be compared with user embeddings in a shared vector space. This intermediary representation allows the system to handle new entities by comparing their embeddings against the pre-built content embedding space, eliminating the need for large historical datasets.
2Measurement precision
If the system processes large volumes of data for personalization, then recommendation quality is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-computing content embeddings and storing them in a ready-to-use format. Instead of processing large volumes of raw data at query time, the content is embedded in advance into a compressed vector representation. This preliminary processing reduces the computational burden during real-time recommendations, as the system only needs to perform similarity comparisons in the embedded space rather than processing raw content and user history data.
Solution Approach 2:
The patent applies parameter changes by transforming content from its original high-dimensional form into a lower-dimensional embedding space. This dimensionality reduction changes the parameters of the data representation, compressing large volumes of content features into compact vectors that retain the essential information needed for similarity comparisons. This parameter transformation reduces processing time while maintaining personalization quality.
3Adaptability or versatility
If the system uses user-to-user learning and profiling for personalization, then recommendations are customized, but user privacy concerns and data security issues arise
Solution Approach 1:
The system uses content embeddings as an intermediary that enables personalization without directly exposing or processing sensitive user data. Instead of storing and analyzing detailed user behavior profiles, the system compares user embeddings (which can be computed on-demand from minimal interactions) against content embeddings in a shared vector space. This intermediary approach allows personalized recommendations while minimizing the collection and storage of sensitive user information, thereby addressing privacy concerns.
Solution Approach 2:
The patent applies self-service by enabling the system to generate user embeddings and perform recommendations based on minimal user input or real-time interactions. Rather than requiring extensive user profiling data that raises privacy issues, the system can compute user preferences on-demand from a small amount of interaction data or even from the user's explicit queries. This self-service capability allows personalization with minimal data collection, reducing privacy risks.
Data Source
AI summary
A system and method for a content-centric personalized recommendation engine that includes processing user data comprised of user feature data as input to a user neural network model and yielding a user embedding; processing the user embedding through a matchmaking neural network, which is a trained model to map user embeddings and content embeddings to a shared dimensional space, and yielding a user shared-item embedding; and applying analysis of the user shared-item embedding in selecting at least one content item associated with a content shared-item embedding within the matchmaking neural network.