Sequence-Aware User Embeddings for Adaptive Content Recommendation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional content recommendation systems fail to adapt to individual user interests by applying a uniform decay function, ignoring the varying intensities of user interests over time, leading to suboptimal content matching.
Innovation Solution
A sequence-aware mechanism that generates user embeddings using a memory-dependency aware model to adaptively decay user interests based on the sequence of events, capturing individualized intensity changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a uniform decay function is applied to user activity data, then the system can adapt to time-changing interests, but it fails to capture individualized intensity variations in user interests
Solution Approach 1:
The patent applies local quality by transitioning from a uniform decay function applied globally to all users to individualized decay functions tailored to each user's specific interest patterns. The system learns unique decay characteristics for each user based on their historical interaction sequences, allowing different parts of the user base to have customized weight allocation over time, thereby capturing individualized intensity variations while maintaining adaptability.
Solution Approach 2:
The patent implements parameter changes by making the decay function parameters (weights at different time instances) dynamic and user-specific rather than fixed and uniform. The system learns optimal decay parameters for each user based on their sequential behavior patterns, allowing the parameters to adapt to individual user characteristics and interest intensity variations, thus resolving the contradiction between uniform adaptability and precise individualization.
2Ease of manufacture
If deterministic weight allocation is used in decay functions, then the system maintains simplicity in implementation, but it cannot adapt to varying user-specific interest patterns
Solution Approach 1:
The patent applies dynamics by transforming the static, deterministic weight allocation into a dynamic, learnable process. Instead of using fixed decay weights, the system employs a sequence-aware model that adapts the weight allocation based on each user's historical interaction sequences. This dynamic approach maintains computational feasibility while significantly improving adaptability to user-specific patterns through learned temporal dependencies.
Solution Approach 2:
The system implements self-service by enabling the decay function to automatically learn and adjust its own parameters based on observed user behavior sequences. The model autonomously optimizes the weight allocation for each user without requiring manual configuration, combining the simplicity of automated parameter learning with the adaptability to individual user patterns.
3Productivity
If conventional decay functions are used, then the system can process user activity data efficiently, but it fails to capture the sequence-aware nature of user interests
Solution Approach 1:
The patent substitutes the mechanical, fixed decay function with a learned, sequence-aware model that processes user activity sequences. Instead of applying predetermined weight allocations, the system uses a neural network-based approach to dynamically compute weights based on the sequential patterns in user interactions, preserving sequence information while maintaining processing efficiency through optimized computational architectures.
Data Source
AI summary
The present teaching relates to content recommendation. Content is selected from multiple pieces of content for recommending to a user based on a user embedding and contextual information. Performance of the user with respect to the recommended content is obtained. The user embedding for the user is adapted based on the performance information via a memory-dependency aware model previously trained to learn past interests of the user and intensities of such past interests. An updated user embedding is generated to represent current interests of the user via the adapted user embedding produced by the memory-dependency aware model according to the known intensities of the past interests of the user as well as user's interest exhibited in the recommended content.