  Recommendation systems (RS) are an increasingly relevant area for both
academic and industry researchers, given their widespread impact on the daily
online experiences of billions of users. One common issue in real RS is the
cold-start problem, where users and items may not contain enough information to
produce high-quality recommendations. This work focuses on a complementary
problem: recommending new users and items unseen (out-of-vocabulary, or OOV) at
training time. This setting is known as the inductive setting and is especially
problematic for factorization-based models, which rely on encoding only those
users/items seen at training time with fixed parameter vectors. Many existing
solutions applied in practice are often naive, such as assigning OOV
users/items to random buckets. In this work, we tackle this problem and propose
approaches that better leverage available user/item features to improve OOV
handling at the embedding table level. We discuss general-purpose plug-and-play
approaches that are easily applicable to most RS models and improve inductive
performance without negatively impacting transductive model performance. We
extensively evaluate 9 OOV embedding methods on 5 models across 4 datasets
(spanning different domains). One of these datasets is a proprietary production
dataset from a prominent RS employed by a large social platform serving
hundreds of millions of daily active users. In our experiments, we find that
several proposed methods that exploit feature similarity using LSH consistently
outperform alternatives on most model-dataset combinations, with the best
method showing a mean improvement of 3.74% over the industry standard baseline
in inductive performance. We release our code and hope our work helps
practitioners make more informed decisions when handling OOV for their RS and
further inspires academic research into improving OOV support in RS.
