Multimodal Content Recommendation Using Simulated User Playback
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Solution Overview
Problem
Traditional content delivery and recommendation systems fail to capture the full spectrum of user preferences across different content modalities, leading to suboptimal recommendations and biases such as recommendation, popularity, and active user biases.
Innovation Solution
A content retrieval system that tracks user interaction behavior to generate a content interaction bot, mimicking user interactions across multiple modalities, and builds a comprehensive user persona for personalized content recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional content delivery and recommendation systems are used, then system simplicity is maintained, but user preference capture across different content modalities is insufficient leading to suboptimal recommendations and biases
Solution Approach 1:
The system segments user interaction analysis into multiple independent modality components (textual, visual, audio, metadata) that can be processed separately and then integrated. This allows comprehensive multi-modal preference capture while maintaining manageable system architecture through modular processing units for each modality type.
Solution Approach 2:
The system transitions from traditional single-dimensional recommendation approaches to multi-dimensional analysis by incorporating multiple content modalities simultaneously. This dimensional expansion enables more precise user preference measurement across different content types without requiring a complete system overhaul, as each modality can be processed through dedicated pipelines.
2Measurement precision
If comprehensive multimodal analysis is implemented, then recommendation relevance is improved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary processing of content items by pre-computing embeddings and extracting key features from multiple modalities before recommendation generation. This advance preparation reduces the computational burden during actual recommendation queries, as the heavy lifting of multimodal analysis has already been completed and stored for efficient retrieval and combination.
3Reliability
If traditional recommendation systems are used, then system complexity is low, but biases such as recommendation, popularity, and active user biases persist
Solution Approach 1:
The system applies different processing weights and attention mechanisms to different content modalities based on user-specific preferences. Rather than treating all modalities uniformly, the system dynamically adjusts the importance of textual, visual, audio, and metadata features according to what each individual user values most, thereby reducing biases while maintaining system manageability through localized customization.
Data Source
AI summary
Disclosed herein are system, apparatus, article-of-manufacture, method and/or computer program product embodiments, and/or combinations/sub-combinations thereof, for personalized multimodal analysis for content item recommendation. An embodiment operates by identifying playback of a first content item by a user device, and simulating playback of a second content item with a modality feature that matches the first content items. Affinity for a modality of the second content item is identified based on weights assigned to the different modalities according to the simulated playback. Respective similarity scores are generated for a plurality of content items based on a similarity between a vector for an embedding indicative of the modality for the second content item and a respective vector for an embedding indicative of the modality generated for the content items. Indication of a set of content items with respective similarity scores that satisfy a similarity score threshold is sent to the user device.