Graph-Based Content Recommendation System Using Topic Groupings
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Solution Overview
Problem
Current content recommendation systems fail to effectively identify and provide contextually and semantically relevant content to users, as they lack efficient methods to analyze and filter term contributions and relationships within large content corpora.
Innovation Solution
A graph-based approach is employed to generate topic groupings from content corpora, where terms are decomposed into refined topic groupings based on a contribution threshold, and metadata is projected into these groupings to identify relevant nodes, enabling the recommendation of content associated with semantically and contextually similar terms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a graph-based approach with topic groupings is used to analyze content corpora, then content recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the content corpus into refined topic groupings by decomposing a graph of terms and relationships. Each topic grouping contains terms that contribute to a specific topic above a threshold, creating organized clusters that improve recommendation accuracy while managing complexity through structured division of the content space.
Solution Approach 2:
The patent introduces an intermediary projection mechanism that maps metadata from consumed content into the refined topic groupings. This intermediary step identifies relevant nodes in the graph by projecting metadata vectors onto the topic space, enabling accurate content matching without requiring direct complex comparisons of entire content corpora.
2Measurement precision
If refined topic groupings with contribution thresholds are implemented, then term relevance is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary decomposition of the content corpus into refined topic groupings with pre-calculated contribution thresholds. By pre-processing the content corpus into organized topic structures before recommendation generation, the system establishes term relevance criteria in advance, reducing processing time during actual content recommendation operations.
Solution Approach 2:
The patent applies parameter changes by using contribution thresholds to filter and refine topic groupings. By adjusting the contribution threshold parameter, the system optimizes the balance between term relevance precision and processing efficiency, retaining only terms that meet the threshold criteria while discarding less relevant terms to reduce processing overhead.
3Measurement precision
If metadata projection into refined topic groupings is performed, then content relevance identification is improved, but computational load increases
Solution Approach 1:
The patent extracts and utilizes only the essential features by projecting metadata into refined topic groupings rather than processing entire content items. This extraction approach focuses computational resources on projecting metadata vectors into the pre-established topic space, identifying relevant nodes without the need for exhaustive comparison of all content attributes, thereby reducing overall computational load.
Data Source
AI summary
Users consume a wide variety of content from various sources, such as videos accessible through websites. As provided herein, content recommendations that are contextually and/or semantically relevant to current content consumed by a user may be identified and provided to the user. For example, metadata for a video being watched by the user may be identified (e.g., terms extracted from a description, user reviews, a category, and/or other information). The metadata may be used to identify content recommendations based upon the metadata corresponding to terms grouped into a set of refined topic groupings of a graph comprising terms and relationships between terms extracted from a content corpus. The metadata may be matched to relevant terms within the set of refined topic groupings, and content recommendations comprising content corresponding to the relevant terms may be suggested to the user.