Personalized and Multi-View Representation for Federated Cold-Start Recommendation

Jaehyung Lim; Wonbin Kweon; Woojoo Kim; Junyoung Kim; Dongha Kim; Hwanjo Yu

cs.IRAug 28, 2026

Personalized and Multi-View Representation for Federated Cold-Start Recommendation

Authors: Jaehyung Lim, Wonbin Kweon, Woojoo Kim, Junyoung Kim, Dongha Kim, Hwanjo Yu

Organizations: Pohang University of Science and Technology Pohang, Republic of Korea · University of Illinois Urbana-Champaign Champaign, IL, USA

Abstract

Federated recommendation (FedRec) enables personalized modeling without centralizing users' interaction histories, but most existing methods assume a fixed item pool and thus overlook the practical cold-item setting where new items continuously arrive. Under the dual-sided constraint, where the server cannot access clients' interactions while clients cannot access the server's proprietary item attribute features, prior federated cold-start recommendation approaches suffer from three structural limitations: a lack of personalization, compositionality failure caused by forcing heterogeneous semantics into a single embedding space, and training- and communication-inefficiency arising from explicit alignment between separate collaborative and attribute representations. To address these challenges, we propose Personalized and Multi-view Representation for Federated Cold-Start Recommendation (PMFRec). PMFRec learns a personalized representation generator to produce user-specific item representations from attribute features, and introduces a global multi-view encoder with item-adaptive gating and an orthogonality objective to capture complementary semantic views while reducing cross-view redundancy. In addition, PMFRec fuses collaborative and attribute knowledge into a single exchanged item representation, eliminating the need for an explicit client-side regularizer and reducing communication overhead. Extensive experiments on real-world datasets show that PMFRec consistently outperforms strong baselines in cold-item recommendation and further improves user-level fairness, warm-scenario adaptability, and robustness under Local Differential Privacy (LDP).

Figures & tables

Category	Drug	ML	Graph	Chemistry	Recommendation
Item A	✓	✓	✓
Item B	✓			✓
Item C		✓	✓		✓

Table 1. Toy example illustrating paper recommendation: each item corresponds to a paper, and the category indicates its research area.

Figure 1. Comparison between existing methods and PMFRec. Prior federated cold-start approaches rely on a global single encoder, resulting in identical representations. In contrast, PMFRec introduces a personalized and multi-view encoding to generate user-specific representations for same cold items.

Figure 2. The overall framework of PMFRec.

Dataset	Split	#Users	#Items	#Interactions	Sparsity
CiteULike	Train	5,551	13,584	164,210	99.78%
	Val	5,551	1,018	13,037	–
	Test	5,551	2,378	27,739	–
XING-5000	Train	5,000	11,290	120,944	99.79%
	Val	5,000	1,869	21,713	–
	Test	5,000	5,648	58,609	–

Table 2. Statistics of the four cold-start recommendation datasets. Sparsity is reported for the training split.

Methods	Metrics	CiteULike			XING-5000			XING-10000			XING-20000
Methods	Metrics	Recall	Precision	NDCG	Recall	Precision	NDCG	Recall	Precision	NDCG	Recall	Precision	NDCG
FedMVMF	@5	0.1378	0.1078	0.1721	0.0083	0.0193	0.0181	0.0031	0.0076	0.0066	0.0067	0.0157	0.0144
	@10	0.2228	0.0927	0.2154	0.0144	0.0168	0.0169	0.0054	0.0066	0.0064	0.0114	0.0136	0.0139
	@20	0.3383	0.0751	0.2670	0.0337	0.0191	0.0307	0.0221	0.0108	0.0286	0.0239	0.0140	0.0213
FedWDR	@5	0.1585	0.1314	0.2049	0.0092	0.0229	0.0227	0.0123	0.0281	0.0276	0.0140	0.0350	0.0318
	@10	0.2507	0.1080	0.2478	0.0173	0.0215	0.0230	0.0213	0.0248	0.0263	0.0236	0.0290	0.2960

Table 3. Performance comparison across four datasets: the best results are highlighted in bold, specifically used when our model’s result is larger than the best-performing baseline where

Improv(\%)

represents the relative performance improvement over the best-performing baseline, and

*

denotes statistical significance (

p\leq 0.05

) for the paired t-test against the best baseline on each metric.

Methods	CiteULike						XING-5000
Methods	R@10	R@20	P@10	P@20	N@10	N@20	R@10	R@20	P@10	P@20	N@10	N@20
PMFRec	0.2929	0.4187	0.1309	0.0979	0.3001	0.3441	0.1530	0.2545	0.1718	0.1411	0.1850	0.2112
w/o $P_{u}$	0.2675	0.3900	0.1200	0.0916	0.2763	0.3234	0.1367	0.2304	0.1553	0.1294	0.1662	0.1952
w/ only $P_{u}$	0.2656	0.3988	0.1179	0.0922	0.2739	0.3244	0.1181	0.1927	0.1334	0.1093	0.1395	0.1625
w/ single-view	0.2886	0.4120	0.1286	0.0964	0.2936	0.3381	0.1498	0.2482	0.1694	0.1386	0.1816	0.2086
w/o gate	0.2895	0.4167	0.1284	0.0972	0.2947	0.3418	0.1523	0.2455	0.1699	0.1373	0.1816	0.2066

Table 4. Ablation study of PMFRec on CiteULike and XING-5000.

Dataset	Metric	IFedNCF	IPFedRec	w/o $P_{u}$	w/ single-view	PMFRec
CiteULike	Z@10	36.93%	37.15%	37.65%	34.89%	34.71 %
CiteULike	Z@20	24.23%	25.73%	25.24%	23.66%	22.92 %
XING-5000	Z@10	30.30%	32.00%	30.34%	28.94%	26.80 %
XING-5000	Z@20	12.12%	11.24%	11.86%	11.46%	11.04 %

Table 5. Comparison of zero-score ratio (Z@K).

Figure 3. Analysis for user-level fairness and performance distribution.

Figure 4. Effect of load balancing loss on CiteULike.

Figure 5. Analysis of cross-view redundancy on CiteULike.

View	Title of literature
View 1	Evolving protein interaction networks through gene duplication
	Light Microscopy Techniques for Live Cell Imaging
	Widespread occurrence of antisense transcription in the human genome
View 2	Thirteen Ways to Look at the Correlation Coefficient
	Information Literacy
	A face(book) in the crowd: social Searching vs. social browsing

Table 6. Qualitative Case Study on CiteULike. The items assigned highest weights for different views reveal distinct semantic clusters (e.g., Biology vs. Social Science), supporting the interpretability of our multi-view decomposition.

Figure 6. Adaptability of personalized and multi-view representations to existing baselines.

Figure 7. Performance in the warm scenario.

Figure 8. Convergence curves on CiteULike.

Figure 9. Parameter sensitivity analysis on CiteULike and XING-5000.

Dataset	Method	IFedNCF		IFedNCF w/ PM		PMFRec
Dataset	Metrics	R@10	N@10	R@10	N@10	R@10	N@10
CiteULike	w/o LDP	0.2774	0.2763	0.2954	0.3037	0.2929	0.3001
	$\varepsilon=4$	0.0928	0.1029	0.1088	0.1147	0.2274	0.2424
	$\varepsilon=8$	0.1556	0.1640	0.1734	0.1776	0.2381	0.2494
	$\varepsilon=10$	0.1785	0.1892	0.1743	0.1781	0.2432	0.2531
XING-5000	w/o LDP	0.1429	0.1726	0.1695	0.2011	0.1530	0.1850

Table 7. Performance under (ε, δ)-LDP with varying privacy budgets ε and fixed

\delta

Figure 10. Qualitative case study under LDP ( $\varepsilon=10$ ). Per-round diagnostics on CiteULike (top) and XING-5000 (bottom) for IFedNCF w/PM.

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