  In this paper, we study item advertisements for small businesses. This
application recommends prospective customers to specific items requested by
businesses. From analysis, we found that the existing Recommender Systems (RS)
were ineffective for small/new businesses with a few sales history. Training
samples in RS can be highly biased toward popular businesses with sufficient
sales and can decrease advertising performance for small businesses. We propose
a meta-learning-based RS to improve advertising performance for small/new
businesses and shops: Meta-Shop. Meta-Shop leverages an advanced meta-learning
optimization framework and builds a model for a shop-level recommendation. It
also integrates and transfers knowledge between large and small shops,
consequently learning better features in small shops. We conducted experiments
on a real-world E-commerce dataset and a public benchmark dataset. Meta-Shop
outperformed a production baseline and the state-of-the-art RS models.
Specifically, it achieved up to 16.6% relative improvement of Recall@1M and
40.4% relative improvement of nDCG@3 for user recommendations to new shops
compared to the other RS models.
