Two-stage system using item features for next-item recommendation

Madiraju Srilakshmi, Gourab Chowdhury, Sudeshna Sarkar

Abstract: Highlights•We address next-item recommendation using user transactions and item features.•Our item embedding captures transaction-based similarity and item features.•The candidate items are generated by predicting next-item features.•Our model addresses the cold-start problem of item.