Generative Recommender System for Personalized Media Content
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current temporally sequenced media, such as videos and podcasts, often contain content that is not relevant to specific users or user segments, necessitating a computer-implemented process for automatically generating engaging content based on semantic understanding.
Innovation Solution
A processor-based method and system that infers a semantic-level understanding of media content and uses this understanding to generate new content elements, employing adaptive systems, fuzzy content networks, and neural networks for personalized recommendations and communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If temporally sequenced media is provided to users, then content availability is improved, but user engagement with relevant content deteriorates due to irrelevance
Solution Approach 1:
The media content is segmented into multiple temporal segments or chapters, allowing the system to identify and isolate specific portions that are most relevant to each user based on their preferences and behavior patterns, thereby improving engagement while maintaining overall content availability
Solution Approach 2:
Different portions of the temporally sequenced media are assigned different qualities or weights based on their relevance to specific users, enabling the system to prioritize and emphasize locally relevant content segments while maintaining the complete media structure
2Reliability
If manual content curation is performed, then content relevance is improved, but system complexity and time consumption worsen
Solution Approach 1:
The system performs automatic content curation and relevance assessment using machine learning models that analyze user behavior patterns, preferences, and media characteristics to autonomously determine content relevance without requiring manual intervention, thereby reducing system complexity while maintaining high relevance accuracy
Solution Approach 2:
The system continuously monitors user interactions with media content and uses this feedback to iteratively improve the relevance assessment models, enabling the system to learn from actual user behavior and refine its content selection algorithms over time without increasing operational complexity
3Adaptability or versatility
If comprehensive media content is provided, then content variety is improved, but user attention span deteriorates due to overwhelming information
Solution Approach 1:
The system applies partial action by selectively presenting only the most relevant content segments to each user based on their individual preferences and current context, rather than providing comprehensive content, thereby preserving user attention span while maintaining adequate content variety through personalized selection
Solution Approach 2:
The content presentation is made dynamic by continuously adjusting which media segments are presented based on real-time user behavior, context changes, and evolving preferences, allowing the system to adapt the content mix to maintain user engagement and attention span while preserving variety
Data Source
AI summary
A generative recommender method and system applies trained neural networks to infer related concepts with respect to segments of temporally sequenced content that are inferred to be of particular interest to users. The inferred related concepts of interest may be embodied, for example, in the form vectorized embeddings of natural language and/or images. The embodied inferred related concepts of interest are then input into a generative process that applies trained neural networks to execute one or more vector embedding-based steps that result in generated content elements such as video that are based upon the related concepts of interest.