INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering

Abstract

Collaborative filtering is one of the most common scenarios and popular research topics in recommender systems. Among existing methods, latent factor models, i.e., learning a specific embedding for each user/item by reconstructing the observed interaction matrix, have shown excellent performances. However, such user-specific and item-specific embeddings are intrinsically transductive, making it difficult to deal with new users and new items unseen during training. Besides, the number of model parameters heavily depends on the number of all users and items, restricting its scalability to real-world applications. To solve the above challenges, in this paper, we propose a novel model-agnostic and scalable Inductive Embedding Module for collaborative filtering, namely INMO. INMO generates the inductive embeddings for users (items) by characterizing their interactions with some template items (template users), instead of employing an embedding lookup table. Under the theoretical analysis, we further propose an effective indicator for the selection of template users/items. Our proposed INMO can be attached to existing latent factor models as a pre-module, inheriting the expressiveness of backbone models, while bringing the inductive ability and reducing model parameters. We validate the generality of INMO by attaching it to both Matrix Factorization (MF) and LightGCN, which are two representative latent factor models for collaborative filtering. Extensive experiments on three public benchmarks demonstrate the effectiveness and efficiency of INMO in both transductive and inductive recommendation scenarios.

Accepted by SIGIR 2022

References in corpus (9)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• KGAT: Knowledge Graph Attention Network for Recommendation
• Simplifying Graph Convolutional Networks
• Graph Convolutional Matrix Completion
• Disentangled Graph Collaborative Filtering
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
• How to Retrain Recommender System? A Sequential Meta-Learning Method
• Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach
• User Embedding based Neighborhood Aggregation Method for Inductive Recommendation

Cited by in corpus (4)

• Inductive Cognitive Diagnosis for Fast Student Learning in Web-Based Online Intelligent Education Systems
• Popularity Debiasing from Exposure to Interaction in Collaborative Filtering
• Reproducibility and Artifact Consistency of the SIGIR 2022 Recommender Systems Papers Based on Message Passing
• The Limits of Graph Samplers for Training Inductive Recommender Systems: Extended results