  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.
