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Denoising Multi-Level Preference Learning for Local Interest-Oriented Session-Based Recommendation
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AI
DOI：10.1111/coin.70213.png)
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Abstract
En 中文
Session-based recommendation (SBR) is an important part of modern recommender systems. It can model user preferences without long-term user profiles. This is useful in scenarios with anonymous users or fast-changing user behaviors. In these cases, long-term histories are missing or unreliable. However, SBR still faces several challenges. Graph-based methods find it hard to capture users' multilayered and diverse interests. Interest shifts also introduce noise within sessions and across sessions. It is also difficult to model a user's immediate intent while keeping stable local interests. We propose the Denoising Multi-Level Preference Learning for Local Interest-oriented Graph Neural Network (DMPL-GNN) to solve these problems. The model has three main parts. First, we design an adaptive graph module with convolutional residual networks. It learns fine-grained local interests. Second, we add a target node and combine graph convolution with sparse attention. This builds a stable representation of global interests. Third, we design an intent fusion module. It treats local interest as the main factor for generating recommendations. Experiments on real-world datasets show that DMPL-GNN performs better than existing methods. Ablation studies also prove the usefulness of each module. Our work offers new ideas for improving session-based recommendation. Our code is available at https://github.com/csgii/DMPL-GNN/tree/main.
Keywords:
local and global interest representations
multi-intent fusion
session-based recommendation