{"type":"ScholarlyArticle","title":"Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning","authors":[{"name":"Zhenghong Lin","slug":"zhenghong-lin","given":"Zhenghong","family":"Lin","affiliation":"College of Computer and Data Science , Fuzhou University , Fuzhou , China"},{"name":"Wei Huang","slug":"wei-huang","given":"Wei","family":"Huang","affiliation":"College of Computer and Data Science , Fuzhou University , Fuzhou , China"},{"name":"Hengyu Zhang","slug":"hengyu-zhang","given":"Hengyu","family":"Zhang","affiliation":"College of Computer and Data Science , Fuzhou University , Fuzhou , China"},{"name":"Jiayu Xu","slug":"jiayu-xu","given":"Jiayu","family":"Xu","affiliation":"College of Computer and Data Science , Fuzhou University , Fuzhou , China"},{"name":"Weiming Liu","slug":"weiming-liu","given":"Weiming","family":"Liu","affiliation":"College of Computer Science and Technology , Zhejiang University , Hangzhou , China"},{"name":"Xinting Liao","slug":"xinting-liao","given":"Xinting","family":"Liao","affiliation":"College of Computer Science and Technology , Zhejiang University , Hangzhou , China"},{"name":"Fan Wang","slug":"fan-wang","given":"Fan","family":"Wang","affiliation":"College of Computer Science and Technology , Zhejiang University , Hangzhou , China"},{"name":"Shiping Wang","slug":"shiping-wang","given":"Shiping","family":"Wang","affiliation":"College of Computer Science and Technology , Zhejiang University , Hangzhou , China"},{"name":"Yanchao Tan","slug":"yanchao-tan","given":"Yanchao","family":"Tan","affiliation":"College of Computer and Data Science , Fuzhou University , Fuzhou , China"}],"abstract":"Recently, dual-target cross-domain recommendation (DTCDR) has been proposed to alleviate the data sparsity problem by sharing the common knowledge across domains simultaneously. However, existing methods often assume that personal data containing abundant identifiable information can be directly accessed, which results in a controversial privacy leakage problem of DTCDR. To this end, we introduce the P2DTR framework, a novel approach in DTCDR while protecting private user information. Specifically, we first design a novel inter-client knowledge extraction mechanism, which exploits the private set intersection algorithm and prototype-based federated learning to enable collaboratively modeling among multiple users and a server. Furthermore, to improve the recommendation performance based on the extracted common knowledge across domains, we proposed an intra-client enhanced recommendation, consisting of a constrained dominant set (CDS) propagation mechanism and dual-recommendation module. Extensive experiments on real-world datasets validate that our proposed P2DTR framework achieves superior utility under a privacy-preserving guarantee on both domains.","doi":"10.24963/ijcai.2024/238","url":"https://ijcai.org/proceedings/2024/238","pdf":"https://ijcai.org/proceedings/2024/0238.pdf","markdown":"https://ijcai.org/proceedings/2024/238.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/238","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":238,"pages":{"first":2153,"last":2161},"keywords":["Data Mining: DM: Recommender systems","Data Mining: DM: Privacy-preserving data mining","Data Mining: DM: Applications"],"videos":[],"references":[{"position":1,"raw":"Methods Game-domain recommendation Video-domain recommendation MRR NDCG HR MRR NDCG HR @5 @10 @1 @5 @10 @5 @10 @1 @5 @10 PMLAM 2.96±0.05 1.99±0.08 2.33±0.08 0.76±0.02 2.89±0.12 6.04±0.09 1.52±0.06 1.34±0.15 1.67±0.12 0.46±0.11 2.64±0.09 4.37±0.10 LightGCN 3.06±0.03 2.07±0.06 2.51±0.14 0.83±0.04 2.95±0.10 6.31±0.08 1.67±0.13 1.39±0.11 1.76±0.06 0.51±0.07 2.71±0.10 4.61±0.12 CGCL 3.20±0.06 2.22±0.04 2.63±0.14 0.99±0.05 3.12±0.09 6.60±0.09 1.87±0.13 1.59±0.12 1.97±0.07 0.53±0.09 2.97±0.14 4.81±0.14 CoNet 3.16±0.11 2.24±0.16 2.85±0.03 0.86±0.06 3.24±0.12 6.60±0.10 1.83±0.11 1.63±0.09 2.03±0.08 0.54±0.13 3.01±0.10 4.96±0.04 ETL 3.32±0.08 2.42±0.07 3.21±0.09 1.02±0.02 3.44±0.09 6.73±0.07 1.98±0.06 1.78±0.12 2.15±0.03 0.59±0.12 3.16±0.13 5.15±0.11 CDRIB 3.50±0.12 2.50±0.15 3.44±0.10 1.12±0.08 3.69±0.04 7.06±0.13 2.28±0.11 1.94±0.08 2.52±0.11 0.65±0.13 3.37±0.12 5.52±0.07 FedGNN 3.48±0.09 2.46±0.13 3.34±0.09 1.09±0.09 3.36±0.05 6.89±0.15 2.05±0.08 1.86±0.15 2.40±0.08 0.60±0.11 3.30±0.10 5.26±0.13 PriCDR 3.62±0.09 2.55±0.09 4.14±0.13 1.15±0.04 4.16±0.16 8.43±0.10 3.14±0.07 2.52±0.06 3.44±0.12 1.08±0.08 4.22±0.08 7.50±0.06 FedCDR 3.66±0.13 2.67±0.06 4.16±0.05 1.20±0.03 4.28±0.06 8.64±0.04 3.30±0.03 2.64±0.10 3.62±0.07 1.13±0.02 4.36±0.12 7.72±0.11 P2FCDR 4.02±0.05 2.75±0.04 4.22±0.03 1.28±0.02 4.64±0.07 8.97±0.08 3.77±0.04 3.20±0.08 4.13±0.03 1.25±0.06 5.03±0.08 8.75±0.06 FPPDM 4.10±0.04 2.80±0.07 4.30±0.05 1.30±0.01 4.68±0.08 9.05±0.05 3.83±0.06 3.29±0.07 4.17±0.05 1.26±0.08 5.23±0.09 8.85±0.07 Ours 4.30±0.06 * 3.10±0.12 * 4.62±0.06 * 1.38±0.03 * 4.97±0.08 * 9.32±0.03 * 4.48±0.11 * 3.64±0.08 * 4.73±0.03 * 1.47±0.06 * 5.80±0.04 * 9.66±0.14 * Improv. 4.88% 10.71% 7.44% 6.15% 6.20% 2.98% 16.97% 10.64% 13.43% 16.67% 10.90% 9.15% Methods Cloth-domain recommendation Sport-domain recommendation MRR NDCG HR MRR NDCG HR @5 @10 @1 @5 @10 @5 @10 @1 @5 @10 PMLAM 2.25±0.12 1.68±0.05 2.13±0.08 0.59±0.11 2.83±0.10 4.73±0.04 2.63±0.06 1.96±0.04 2.95±0.05 0.72±0.13 3.26±0.05 7.06±0.07 LightGCN 2.36±0.08 1.75±0.04 2.29±0.12 0.65±0.05 2.97±0.07 4.84±0.13 2.72±0.03 2.07±0.06 3.02±0.07 0.78±0.16 3.39±0.15 7.20±0.05 CGCL 2.47±0.03 1.83±0.08 2.40±0.14 0.68±0.09 3.11±0.13 5.05±0.07 2.84±0.07 2.16±0.10 3.15±0.16 0.81±0.09 3.54±0.04 7.31±0.10 CoNet 2.41±0.08 1.81±0.09 2.35±0.15 0.67±0.06 2.95±0.12 5.13±0.14 2.96±0.06 2.12±0.11 3.20±0.12 0.81±0.13 3.47±0.12 7.21±0.16 ETL 2.55±0.07 1.90±0.11 2.49±0.06 0.70±0.13 3.01±0.08 5.28±0.09 3.04±0.04 2.16±0.09 3.26±0.11 0.84±0.08 3.51±0.10 7.27±0.05 CDRIB 2.94±0.03 2.06±0.12 2.83±0.10 0.91±0.10 3.25±0.11 5.81±0.07 3.37±0.11 2.36±0.12 3.53±0.06 0.97±0.09 3.71±0.13 7.51±0.03 FedGNN 2.45±0.08 1.86±0.05 2.36±0.12 0.70±0.13 3.15±0.13 5.18±0.08 2.90±0.10 2.47±0.14 3.28±0.13 0.84±0.09 4.30±0.12 6.70±0.09 PriCDR 2.62±0.13 1.95±0.07 2.56±0.13 0.73±0.04 3.31±0.16 5.39±0.06 3.03±0.09 2.63±0.08 3.36±0.14 0.86±0.15 4.44±0.09 6.81±0.10 FedCDR 2.69±0.09 2.06±0.06 2.72±0.03 0.78±0.05 3.39±0.06 5.56±0.09 3.14±0.06 2.67±0.07 3.49±0.05 0.90±0.08 4.47±0.08 7.18±0.03 P2FCDR 2.99±0.11 2.36±0.10 3.08±0.12 0.98±0.02 3.82±0.02 6.26±0.12 3.52±0.02 2.75±0.03 3.70±0.03 1.00±0.04 4.50±0.02 7.36±0.07 FPPDM 3.03±0.11 2.41±0.09 3.11±0.13 0.99±0.03 3.84±0.04 6.36±0.14 3.54±0.03 2.78±0.05 3.74±0.02 1.16±0.03 4.53±0.03 7.40±0.10 Ours 3.54±0.12 * 2.71±0.05 * 3.68±0.04 * 1.14±0.07 * 4.37±0.06 * 7.32±0.11 * 3.64±0.11 * 2.88±0.05 * 3.85±0.04 * 1.22±0.06 * 4.66±0.06 * 7.66±0.05 * Improv. 16.83% 12.45% 18.33% 15.15% 13.80% 15.09% 2.82% 3.60% 2.94% 5.17% 2.87% 3.51% References [Alemu et al., 2019] Leulseged Tesfaye Alemu, Mubarak Shah, and Marcello Pelillo. 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