MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation

Abstract

A common challenge for most current recommender systems is the cold-start problem. Due to the lack of user-item interactions, the fine-tuned recommender systems are unable to handle situations with new users or new items. Recently, some works introduce the meta-optimization idea into the recommendation scenarios, i.e. predicting the user preference by only a few of past interacted items. The core idea is learning a global sharing initialization parameter for all users and then learning the local parameters for each user separately. However, most meta-learning based recommendation approaches adopt model-agnostic meta-learning for parameter initialization, where the global sharing parameter may lead the model into local optima for some users. In this paper, we design two memory matrices that can store task-specific memories and feature-specific memories. Specifically, the feature-specific memories are used to guide the model with personalized parameter initialization, while the task-specific memories are used to guide the model fast predicting the user preference. And we adopt a meta-optimization approach for optimizing the proposed method. We test the model on two widely used recommendation datasets and consider four cold-start situations. The experimental results show the effectiveness of the proposed methods.

References in corpus (5)

• Generalizing from a Few Examples: A Survey on Few-Shot Learning
• Federated Meta-Learning with Fast Convergence and Efficient Communication
• Meta-Learning: A Survey
• Sequential Scenario-Specific Meta Learner for Online Recommendation
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Cited by in corpus (7)

• Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks
• Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion
• CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process
• Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer
• Action Sequence Augmentation for Early Graph-based Anomaly Detection
• Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation
• Proxy-based Item Representation for Attribute and Context-aware Recommendation