Beyond Centralization: User-Controlled Federated Recommendations in Practice

2026-05-14T08:52:18Zf13e719dfe7a3eb06c0cd264cfe587070d7f7a91a98c6a525d6a086ed070f988
GEOLLM-agentsMLPRAGXRootDcontextual-retrievalconversational-recommendersdynamic-content-expirationecosystem-influenceembeddingsenterprise-governancefederated-learninggenerative-recommendationimage-retrievalinformation-retrievalmodel-distillationmultilingual-embeddingsopen-sourceprivacy-preservingprofile-generationrecommender-systemsreproducibilitytheorem-proving-retrievaluser-simulationweb-manipulation

What happened

Collection of recent arXiv papers (May 14, 2026) covering advances in recommender systems, retrieval, and web-enabled LLM agents. Key themes: user-controlled, privacy-preserving federated recommendations deployed live; MLP-based distillation (SID-MLP) to accelerate generative recommenders; ecosystem-level manipulation/optimization of web evidence for LLM search agents (EcoGEO/TRACE); context-dependent image meaning and retrieval; LLM-driven query-aware dynamic content expiration (industrial deployment at Baidu); standardized reproducibility evaluation for conversational recommenders (ReDial);检

Why it matters

A reviewed impact interpretation has not been published for this record.

Evidence and limitations

Source ID
arxiv_cs_ir
Record identifier
f13e719dfe7a3eb06c0cd264cfe587070d7f7a91a98c6a525d6a086ed070f988
Enrichment time
2026-05-14T08:52:18Z
AI-assisted enrichment
Yes

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