Attention-Weighted User Embeddings for Group Preference Prediction
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Solution Overview
Problem
Existing group recommendation systems struggle to accurately predict preferences of large, geographically dispersed user groups on e-commerce sites due to lack of direct interrelationships among users and sparse interaction data, leading to reduced accuracy and ineffective item recommendations.
Innovation Solution
An information processing apparatus and method that encode user behavior histories, assign weights based on item price and behavior history, and aggregate embeddings to generate a representative preference for user groups, using machine learning models with attention mechanisms to identify influential users and predict item recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If users are segmented into large, geographically dispersed user groups on e-commerce sites, then the coverage of recommendation systems is improved, but the accuracy of predicting group preferences deteriorates due to lack of direct interrelationships among users
Solution Approach 1:
The patent introduces an intermediary mechanism (attention-based weighting system) to mediate between individual user preferences and group preference predictions. The system uses learned attention weights as intermediaries to aggregate individual user embeddings, allowing the model to capture indirect relationships and patterns even in geographically dispersed user groups where direct interrelationships are sparse.
Solution Approach 2:
The patent changes the parameter of user aggregation by transitioning from uniform weighting to dynamic attention-based weighting. The attention weights are learned parameters that adaptively adjust the contribution of each user's preference to the group preference prediction, enabling accurate predictions even when users lack direct interrelationships.
2Device complexity
If uniform weighting is used to aggregate user preferences in user groups, then the simplicity of the recommendation system is maintained, but the accuracy of preference prediction deteriorates due to inability to identify influential users
Solution Approach 1:
The patent transforms the static uniform weighting approach into a dynamic attention-based weighting system. The attention weights are computed dynamically based on the input data, allowing the system to adaptively identify and weight influential users according to their actual impact on group preferences, thereby improving prediction accuracy without significantly increasing system complexity.
Solution Approach 2:
The system implements self-service through automatic attention weight computation. The model automatically learns and assigns appropriate weights to different users based on their behavioral patterns and relationships, eliminating the need for manual intervention or complex configuration to identify influential users.
3Measurement precision
If attention-based weighting is used to identify influential users, then the accuracy of group preference prediction is improved, but the computational complexity of the recommendation system increases
Solution Approach 1:
The patent segments the computational process into distinct stages: individual user embedding generation, attention weight computation, and weighted aggregation. This segmentation allows for efficient parallel computation and optimization at each stage, reducing the overall computational complexity while maintaining high prediction accuracy.
4Ease of manufacture
If user groups are formed based on minimal attributes (age, region), then the ease of creating user groups is improved, but the commonality of preferences among users deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-computing user embeddings that capture comprehensive user preferences and behaviors before grouping. This allows the system to work with diverse user groups formed on minimal attributes while still achieving accurate preference predictions through the attention-based aggregation of pre-processed user representations.
Data Source
AI summary
In order to predict a preference that is representative of a group to which users belong more appropriately with higher accuracy based on preferences of the users for items, disclosed herein is an information processing apparatus, comprising: an encoding unit configured to encode a behavior history of a user for an item to generate a first embedding for each user; a weighting unit configured to derive a weight for each user based on information on a price of the item and the behavior history, and weight the first embedding generated by the encoding unit with the derived weight; and an aggregation unit configured to aggregate the first embedding weighted by the weighting unit to generate, for a group to which the user belongs, a second embedding indicating a preference of the group for the item.