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Found 6 papers, 2 papers with code
Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation
Modern listwise recommendation systems need to consider both long-term user perceptions and short-term interest shifts.
Intra-session Context-aware Feed Recommendation in Live Systems
Feed recommendation allows users to constantly browse items until feel uninterested and leave the session, which differs from traditional recommendation scenarios.
Deep Unified Representation for Heterogeneous Recommendation
Recommendation system has been a widely studied task both in academia and industry.
Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation
Inspired by the idea of vector quantization that uses cluster centroids to approximate items, we propose LISA (LInear-time Self Attention), which enjoys both the effectiveness of vanilla self-attention and the efficiency of sparse attention.
Rethinking Lifelong Sequential Recommendation with Incremental Multi-Interest Attention
With the rapid development of these services in the last two decades, users have accumulated a massive amount of behavior data.
Federated Graph Learning -- A Position Paper
Federated learning (FL) is a an emerging technique that can collaboratively train a shared model while keeping the data decentralized, which is a rational solution for distributed GNN training.
