mlsanityData to 7 Oct 2026

Personalized Federated Domain Adaptation for Item-to-Item Recommendation

arXiv 2306.03191 ↗First posted 5 Jun 2023cs.IR cs.LGcited by 4

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Abstract (arXiv)

Item-to-Item (I2I) recommendation is an important function in most recommendation systems, which generates replacement or complement suggestions for a particular item based on its semantic similarities to other cataloged items. Given that subsets of items in a recommendation system might be co-interacted with by the same set of customers, graph-based models, such as graph neural networks (GNNs), provide a natural framework to combine, ingest and extract valuable insights from such high-order relational interactions between cataloged items, as well as their metadata features, as has been shown in many recent studies. However, learning GNNs effectively for I2I requires ingesting a large amount of relational data, which might not always be available, especially in new, emerging market segments. To mitigate this data bottleneck, we postulate that recommendation patterns learned from existing mature market segments (with private data) could be adapted to build effective warm-start models for emerging ones. To achieve this, we propose and investigate a personalized federated modeling framework based on GNNs to summarize, assemble and adapt recommendation patterns across market segments with heterogeneous customer behaviors into effective local models. Our key contribution is a personalized graph adaptation model that bridges the gap between recent literature on federated GNNs and (non-graph) personalized federated learning, which either does not optimize for the adaptability of the federated model or is restricted to local models with homogeneous parameterization, excluding GNNs with heterogeneous local graphs.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 28 of its 36 references that are in the map. First posted on arXiv 5 Jun 2023.

Builds on

28 of 36 references.

References · 28

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16 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.

4 papers.

  1. 2025Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Datacited by 9
  2. 2025Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Datacited by 7
  3. 2026Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Datacited by 3
  4. 2023Pre-trained Recommender Systems: A Causal Debiasing Perspectivecited by 2