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Found 4 papers, 1 papers with code
Prompt-based Multi-interest Learning Method for Sequential Recommendation
Multi-interest learning method for sequential recommendation aims to predict the next item according to user multi-faceted interests given the user historical interactions.
Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems From a Multi-task Perspective
Embedding-based retrieval (EBR) methods are widely used in modern recommender systems thanks to its simplicity and effectiveness.
Latent Evolution Model for Change Point Detection in Time-varying Networks
Our method focuses on learning the low-dimensional representations of networks and capturing the evolving patterns of these learned latent representations simultaneously.
Dual Preference Distribution Learning for Item Recommendation
Moreover, we can summarize a preferred attribute profile for each user, depicting his/her preferred item attributes.
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