Embedding Vector Clustering for Content Recommendation
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Solution Overview
Problem
Identifying and recommending interesting and engaging content to online service subscribers from a vast corpus of content items is challenging due to the difficulty in determining content relevance to individual user interests.
Innovation Solution
The system generates representative embedding vectors for subscribers based on their prior interactions, clusters these vectors into interest clusters, and selects content items similar to these vectors for recommendation, ensuring that recommended content aligns closely with the subscriber's interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If content items are recommended from a vast corpus of millions or billions of items, then the quantity of recommended content increases, but the difficulty of identifying relevant content increases
Solution Approach 1:
The patent replaces manual or rule-based content recommendation mechanisms with a machine learning system that uses embedding vectors and similarity measures. The system automatically processes vast quantities of content items by transforming them into vector representations, enabling efficient computational comparison and relevance detection without manual intervention.
Solution Approach 2:
The patent transforms content items and user profiles into embedding vectors, changing the parameter space from discrete content attributes to continuous vector representations. This parameter transformation enables the system to handle vast quantities of content by converting complex content descriptions into comparable numerical vectors that can be efficiently processed and compared using similarity measures.
2Measurement precision
If content recommendation accuracy is improved by analyzing user interests, then the relevance of recommended content increases, but the complexity of the recommendation system increases
Solution Approach 1:
The patent introduces embedding vectors as intermediary representations between raw content data and recommendation decisions. These vectors serve as a mediating layer that captures essential content characteristics and user interests in a standardized format, simplifying the comparison process while maintaining high recommendation accuracy through similarity measures.
Solution Approach 2:
The embedding vector representation serves multiple functions simultaneously: it encodes content characteristics, represents user interests, enables similarity computation, and supports scalable processing. This universal representation approach allows the system to achieve high recommendation accuracy without proportionally increasing system complexity, as the same vector mechanism handles multiple tasks.
Data Source
AI summary
Systems and methods for recommending content to an online service subscriber are presented. For each subscriber, content items that were the subject of the subscriber's prior interactions are projected, via associated embedding vectors, into a content item embedding space. The content items, via their projections into the content item embedding space, are clustered to form a plurality of interest clusters for the subscriber. A representative embedding vector is determined for each interest cluster, and a plurality of these embedding vectors are stored as the representative embedding vectors for the subscriber. The online service, in response to a request for recommended content for a subscriber, selects a first representative embedding vector associated with the subscriber and identifies a new content item from a corpus of content items according to a similarity measure between the first representative embedding vector and an embedding vector associated with the new content item.