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Found 3 papers, 3 papers with code
Debiasing Recommendation with Personal Popularity
Many methods have been proposed to reduce GP bias but they fail to notice the fundamental problem of GP, i. e., it considers popularity from a \textit{global} perspective of \textit{all users} and uses a single set of popular items, and thus cannot capture the interests of individual users.
Multi-domain Recommendation with Embedding Disentangling and Domain Alignment
We propose a new MDR method named EDDA with two key components, i. e., embedding disentangling recommender and domain alignment, to tackle the two challenges respectively.
Automatic Meta-Path Discovery for Effective Graph-Based Recommendation
Specifically, we define a vector encoding for meta-paths and design a policy network to extend meta-paths.
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