Transformer Sequence Models for User Intent Prediction
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Solution Overview
Problem
Recommender systems face challenges in accurately predicting user intent and actions by ignoring the order of interactions across multiple domains, leading to incomplete understanding of user behavior and intent, especially for users with limited engagements in specific domains.
Innovation Solution
Utilizing session sequence data to create a universal user representation that captures sequential dependencies through transformer-based sequence models, incorporating action-related features like service, action type, item, and actor, to predict future actions and clarify user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recommender systems use traditional methods that ignore interaction order, then system complexity is reduced, but user intent prediction accuracy deteriorates
Solution Approach 1:
The system performs preliminary encoding of user interactions into sequential representations before recommendation generation. Transformer-based sequence models pre-process interaction data to capture order dependencies, creating enriched user representations that feed into the recommendation engine, thereby improving prediction accuracy without increasing real-time complexity
Solution Approach 2:
Sequential representations act as an intermediary between raw interaction data and recommendation algorithms. The transformer models generate intermediate user embeddings that encode interaction order, serving as a bridge that translates complex sequential patterns into features usable by standard recommender systems
2Loss of information
If session sequence data is incorporated to capture interaction order, then user behavior understanding is improved, but data processing complexity increases
Solution Approach 1:
The system segments user interaction data into discrete action-related features (service, action type, item, actor) and processes each through dedicated encoding pathways. This segmentation allows parallel processing of different feature types while maintaining their sequential relationships, reducing overall processing complexity
Solution Approach 2:
The transformer-based sequence models transform interaction sequences into fixed-dimensional user embeddings, changing the parameter representation from variable-length sequences to fixed-size vectors. This parameter transformation enables efficient storage and processing while preserving sequential information
3Measurement precision
If transformer-based sequence models are used to encode user interactions, then prediction of future actions is improved, but computational resources required increase
Solution Approach 1:
User embeddings are pre-computed and stored during offline processing, so that during online recommendation serving, the system only needs to retrieve and combine pre-generated embeddings rather than performing full transformer encoding. This shifts computational burden to offline batch processing, reducing real-time resource consumption
Solution Approach 2:
The system creates and stores copies of user embeddings generated by transformer models. These pre-computed embedding copies are cached and reused across multiple recommendation queries, avoiding repeated expensive transformer computations for the same user interaction patterns
Data Source
AI summary
In an example embodiment, a user's session sequence data is utilized to provide a universal member representation that achieves one or more of the following goals:1. Provides a user-level representation that enables the prediction of future actions based on historical interactions within different domains2. Provides a user representation that allows better clarification of user intent (e.g., network builder, job seeker, profile scraper, etc.)3. Members with similar/behaviors/intent are easily identified4. Less sensitivity to activity levels of members.