Meta-Shop: Improving Item Advertisement For Small Businesses

TL;DR

Meta-Shop introduces a meta-learning recommender system that significantly enhances advertising effectiveness for small and new businesses by transferring knowledge from larger shops, outperforming existing models in real-world tests.

Contribution

The paper presents a novel meta-learning approach for shop-level recommendations, addressing bias issues and improving performance for small businesses in e-commerce.

Findings

Meta-Shop achieved up to 16.6% improvement in Recall@1M.

Meta-Shop achieved up to 40.4% improvement in nDCG@3.

Outperformed baseline and state-of-the-art recommender systems.

Abstract

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.