Localized Self-Attention Module for Sequential Recommendation
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Solution Overview
Problem
Conventional self-attentive (transformer-based) techniques fail to accurately capture short-term dynamics in search queries due to their global nature, leading to inefficient use of computation resources and inaccurate search results when employed in digital content searches.
Innovation Solution
Incorporating a localized self-attention module within a multi-head self-attention module that applies local constraints, such as using model-based or masking-based local encoders, to assign weights based on the recency of interactions, thereby improving accuracy and computational performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional self-attentive (transformer-based) techniques are used to leverage sequences of interactions, then the system can process sequential data, but the accuracy is insufficient and computational resources are wasted due to repeated searches
Solution Approach 1:
The patent segments the self-attention mechanism into localized components with different receptive fields. The localized self-attention module divides the sequence processing into local windows or regions, each handling specific portions of the input sequence independently. This segmentation allows the system to capture short-term dynamics efficiently while reducing redundant computations across the entire sequence, thereby improving both accuracy and computational efficiency.
Solution Approach 2:
The patent applies local quality by assigning different attention characteristics to different parts of the sequence. The localized self-attention module uses position-specific bias terms and local encoders that adapt to the specific characteristics of each local region. This allows the system to focus computational resources on locally relevant patterns while maintaining overall sequence understanding, resolving the contradiction between accuracy and efficiency.
2Measurement precision
If conventional self-attentive techniques with global attention are used, then the system can capture long-range dependencies, but they fail to accurately capture short-term dynamics due to their global nature
Solution Approach 1:
The patent segments the attention computation into localized regions with distinct encoders. Each local encoder processes a specific portion of the sequence with dedicated parameters, preventing the dilution of short-term interaction information that occurs in global attention mechanisms. This segmentation ensures that short-term dynamics are captured with high fidelity while still allowing long-range dependencies to be modeled through the hierarchical structure.
Solution Approach 2:
The patent introduces a localization dimension to the attention mechanism by adding position-specific bias terms and using local encoders that operate in a localized subspace. This dimensional transformation allows the system to simultaneously capture both short-term and long-term dependencies by operating at multiple scales, resolving the information loss problem while maintaining comprehensive sequence understanding.
Data Source
AI summary
Digital content search techniques are described. In one example, the techniques are incorporated as part of a multi-head self-attention module of a transformer using machine learning. A localized self-attention module, for instance, is incorporated as part of the multi-head self-attention module that applies local constraints to the sequence. This is performable in a variety of ways. In a first instance, a model-based local encoder is used, examples of which include a fixed-depth recurrent neural network (RNN) and a convolutional network. In a second instance, a masking-based local encoder is used, examples of which include use of a fixed window, Gaussian initialization, and an adaptive predictor.