Shared Item Embeddings for Cold-Start Recommendation Training
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Solution Overview
Problem
Existing recommender systems struggle with cold-start items due to insufficient training data, leading to inefficient and computationally intensive training processes, especially when predicting user-item interactions.
Innovation Solution
A recommender system that shares item representations generated by an item neural network encoder with a user neural network encoder, using these shared representations as weights in the first layer of the user encoder, reducing the need for random initialization and optimizing training iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of user and item parameters increases to handle more entities in the recommender system, then the prediction accuracy and coverage improve, but the computational intensity and training time increase linearly
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing item embeddings using only item side information before the main training process. This pre-computation step creates a foundation that eliminates the need to learn item parameters from scratch during training, significantly reducing the computational burden and training time when handling large numbers of items while maintaining system coverage
Solution Approach 2:
The patent segments the parameter learning process into two distinct phases: (1) pre-computation of item embeddings from side information, and (2) training of user parameters and interaction predictions. This segmentation allows item parameters to be prepared independently in advance, decoupling their computation from the main training loop and enabling scalable handling of large item catalogs without proportionally increasing training time
2Measurement precision
If more training data is used to improve prediction accuracy, then the model performance improves, but the computational intensity increases
Solution Approach 1:
By pre-computing item embeddings from side information before training, the system eliminates the need to process and learn item parameters during training iterations. This preliminary action reduces the computational workload per training step, allowing the model to utilize more training data for improving prediction accuracy without proportionally increasing computational intensity
Solution Approach 2:
The patent extracts item parameter learning from the main training process by pre-computing item embeddings independently from item side information. This extraction removes a significant computational burden from each training iteration, enabling more efficient use of training data and reducing overall computational intensity while maintaining or improving prediction accuracy
3Adaptability or versatility
If traditional neural network encoders with randomly initialized weights are used, then the system can handle general cases, but cold-start items suffer from insufficient feedback history leading to poor predictions
Solution Approach 1:
The patent applies preliminary action by pre-computing item embeddings from side information before the main training process. This pre-computation provides meaningful initial representations for cold-start items that have insufficient feedback history, enabling the system to make reliable predictions for these items while maintaining general applicability through the same framework
Solution Approach 2:
The patent introduces item embeddings computed from side information as an intermediary representation that bridges the gap for cold-start items. These pre-computed embeddings serve as a mediator that provides meaningful initial representations when feedback history is insufficient, enabling reliable predictions without requiring extensive interaction data
Data Source
AI summary
The present disclosure relates to an improved machine learning-based recommender system and method for cold-start predictions on an ecommerce platform. The improved system predicts user-item interactions with respect to cold-start items in which only side information is available. Item representations generated by an item neural network encoder from item side information are shared with a user neural network. The item representations are used, along with user feedback history, to generate user representations. Specifically, a weight matrix in the first layer of the user neural network encoder is fixed with the shared item embeddings. The effect of this is that, when the user neural network encoder is applied to an input user-item interaction vector, the output of the first layer of the user neural network encoder is a function of the item representations of the items for which the user provided positive feedback. The result is a recommender system that achieves better performance for cold-start items with fewer training iterations.