Price-guided user attention in large-scale E-commerce group recommendation

TL;DR

This paper introduces a novel group recommendation method that incorporates item price as a guiding factor for user attention, improving accuracy in large-scale E-commerce settings by adaptively adjusting user influence based on price.

Contribution

The paper proposes a new price-guided user attention mechanism that enhances existing attention-based group recommenders by integrating item price information through an adaptive sigmoid function.

Findings

Outperforms state-of-the-art methods in hit ratio

Reduces mean square error in recommendations

Effective on both benchmark and real-world datasets

Abstract

Existing group recommender systems utilize attention mechanisms to identify critical users who influence group decisions the most. We analyzed user attention scores from a widely-used group recommendation model on a real-world E-commerce dataset and found that item price and user interaction history significantly influence the selection of critical users. When item prices are low, users with extensive interaction histories are more influential in group decision-making. Conversely, their influence diminishes with higher item prices. Based on these observations, we propose a novel group recommendation approach that incorporates item price as a guiding factor for user aggregation. Our model employs an adaptive sigmoid function to adjust output logits based on item prices, enhancing the accuracy of user aggregation. Our model can be plugged into any attention-based group recommender system if the price information is available. We evaluate our model's performance on a public benchmark and a real-world dataset. We compare it with other state-of-the-art group recommendation methods. Our results demonstrate that our price-guided user attention approach outperforms the state-of-the-art methods in terms of hit ratio and mean square error.