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.
Photo by Masakaze Kawakami on Unsplash
Why is it important?
We use a tree structure to model users intentions explicitly or implicitly.
Read the Original
This page is a summary of: Learning a Hierarchical Intent Model for Next-Item Recommendation, ACM Transactions on Information Systems, April 2022, ACM (Association for Computing Machinery), DOI: 10.1145/3473972.
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