Learning a Hierarchical Intent Model for Next-Item Recommendation

What is it about?

We design a framework modeling user intent and preference explicitly, which empowers the two factors to play their distinctive roles. Accordingly, we propose a key-array memory network (KA-MemNN) with a hierarchical intent tree to model coarse-to-fine user intents. The two-layer weighting unit (TLWU) in KA-MemNN detects user intents and generates intent-specific user profiles. Furthermore, the hierarchical semantic component (HSC) integrates multiple sets of intent-specific user profiles along with different user intent distributions to model a multi-intent user profile. The experimental results on real-world datasets demonstrate the superiority of KA-MemNN over selected state-of-the-art methods.

Featured Image

Photo by Masakaze Kawakami on Unsplash

Photo by Masakaze Kawakami on Unsplash

Why is it important?

We use a tree structure to model users intentions explicitly or implicitly.

Perspectives