auto update @ 2024-07-14T15:11:56Z Asia/Shanghai

Author: youngfish42

README.md

@@ -1423,10 +1423,10 @@ Federated Learning papers accepted by top Information Retrieval conference and j
 
 |Title                                                           |    Affiliation                        |    Venue    |    Year    |    Materials|
 | ------------------------------------------------------------ | ------------------------------- | ----- | ---- | ------------------------------------------------------------ |
-|ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User Benefit | THU | SIGIR | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3626772.3657763)] |
-|Revisit Targeted Model Poisoning on Federated Recommendation: Optimize via Multi-objective Transport | ZJU | SIGIR | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3626772.3657764)] |
-|FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation | UQ | SIGIR | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3626772.3657853)] [PDF](https://arxiv.org/abs/2402.11891) [CODE](https://github.com/ielab/FeB4RAG) |
-|FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction | Alibaba Group | SIGIR | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3626772.3657941)] |
+| ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User Benefit | THU | SIGIR | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3626772.3657763)] |
+| Revisit Targeted Model Poisoning on Federated Recommendation: Optimize via Multi-objective Transport | ZJU | SIGIR | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3626772.3657764)] |
+| FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation | UQ | SIGIR | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3626772.3657853)] [[PDF](https://arxiv.org/abs/2402.11891)] [[CODE](https://github.com/ielab/FeB4RAG)] |
+| FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction | Alibaba Group | SIGIR | 2024 | [[PUB](https://dl.acm.org/doi/10.1145/3626772.3657941)] |
 | Personalized Federated Relation Classification over Heterogeneous Texts | NUDT | SIGIR | 2023 | [[PUB](https://dl.acm.org/doi/10.1145/3539618.3591748)] |
 | Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity | SDU | SIGIR | 2023 | [[PUB](https://dl.acm.org/doi/10.1145/3539618.3591688)] |
 | Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures | UQ | SIGIR | 2023 | [[PUB](https://dl.acm.org/doi/10.1145/3539618.3591722)] [[PDF](https://arxiv.org/abs/2304.03054)] |

data.yaml

@@ -13551,6 +13551,36 @@ fl-in-top-ir-conference-and-journal:
     year: 4
     materials: 60
   body:
+  - title: 'ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set
+      User Benefit'
+    affiliation: THU
+    venue: SIGIR
+    year: '2024'
+    materials:
+      PUB: https://dl.acm.org/doi/10.1145/3626772.3657763
+  - title: 'Revisit Targeted Model Poisoning on Federated Recommendation: Optimize
+      via Multi-objective Transport'
+    affiliation: ZJU
+    venue: SIGIR
+    year: '2024'
+    materials:
+      PUB: https://dl.acm.org/doi/10.1145/3626772.3657764
+  - title: 'FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented
+      Generation'
+    affiliation: UQ
+    venue: SIGIR
+    year: '2024'
+    materials:
+      PUB: https://dl.acm.org/doi/10.1145/3626772.3657853
+      PDF: https://arxiv.org/abs/2402.11891
+      CODE: https://github.com/ielab/FeB4RAG
+  - title: 'FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through
+      Rate Prediction'
+    affiliation: Alibaba Group
+    venue: SIGIR
+    year: '2024'
+    materials:
+      PUB: https://dl.acm.org/doi/10.1145/3626772.3657941
   - title: Personalized Federated Relation Classification over Heterogeneous Texts
     affiliation: NUDT
     venue: SIGIR