Details

Autor(en) / Beteiligte

• Ahsan, Unaiza
• Cui, Xiquan
• West, Rebecca
• Guo, Mingming
• Wu, San He
• Al Jadda, Khalifeh

Titel

Complementary Recommendations Using Deep Multi-modal Embeddings For Online Retail

Ist Teil von

• 2020 IEEE International Conference on Big Data (Big Data), 2020, p.1774-1779

Ort / Verlag

IEEE

Erscheinungsjahr

2020

Link zum Volltext

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Recommendation systems have been crucial in driving revenue in e-commerce especially in online retail. Complementary item recommendation is a challenging problem within this field due to the inherent difficulty in defining how products relate to each other. In this paper we design and propose a complementary item recommendation system that uses multi-modal embeddings (both text-based and image-based). Our system is carefully designed and validated by domain experts. We provide extensive insights into our design choices and what does not work in terms of model choice and features used. We successfully demonstrate our system on two challenging datasets from a home improvement retailer: one involving outdoor furniture (patio) and the second involving bathroom products. When deployed live on the website, our model resulted in +170% increase in engaged visits to the recommendation container.