arXiv subjects

Woojoo Kim

Publications and source records attributed to Woojoo Kim.

7 recordsLinked to original sources

arXiv2026

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

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).

cs.IR↗

arXiv2026

From Overlooked to Explored: Recovering Item Relations via Mixture of Perspectives for Sequential Recommendation

Capturing user preference from a user's interaction sequence is the central challenge of Sequential Recommendation (SR). This preference intuitively emerges from inter-item relations: each item transition reflects a preference embedded in the relations between items, making the faithful capture of these relations essential for accurate recommendation. For this reason, self-attention is dominant in sequential recommendation for its ability to compute pairwise item interactions, yet our empirical analysis reveals that it consistently suffers from similarity bias across various types of transformer-based SR models: dot-product attention scores disproportionately favor similar items, systematically overlooking heterogeneous relations with meaningful preference signals and directly limiting recommendation performance. To address this, we propose PRISM (Perspective-based Relational Insight Synthesis Module), a module that re-examines item relations from multiple perspectives. PRISM employs K Perspective Lenses to calibrate attention from distinct viewpoints, combining an Affinity View that refines homogeneous relations and a Contrast View that exposes heterogeneous ones suppressed by similarity bias, enabling the model to capture the full spectrum of user preferences. Extensive experiments on seven real-world benchmarks demonstrate that PRISM consistently outperforms state-of-the-art baselines. Our code is available at https://github.com/327aem/PRISM/.

cs.IR↗

arXiv2026

VLM2Rec: Resolving Modality Collapse in Vision-Language Model Embedders for Multimodal Sequential Recommendation

Sequential Recommendation (SR) in multimodal settings typically relies on small frozen pretrained encoders, which limits semantic capacity and prevents Collaborative Filtering (CF) signals from being fully integrated into item representations. Inspired by the recent success of Large Language Models (LLMs) as high-capacity embedders, we investigate the use of Vision-Language Models (VLMs) as CF-aware multimodal embedders for SR. However, we find that standard contrastive Supervised Fine-Tuning (SFT), used to adapt VLMs for embedding generation and inject CF signals, can amplify inherent modality imbalance: optimization becomes dominated by one modality while the other degrades, ultimately undermining recommendation accuracy. To address this, we propose VLM2Rec, a VLM embedder-based framework for multimodal sequential recommendation designed to promote balanced modality utilization. Specifically, we introduce Weak-modality Penalized Contrastive Learning to mitigate gradient imbalance during optimization and cross-modal Relational Topology Regularization to preserve geometric consistency between modalities. Experiments show that VLM2Rec consistently improves over strong baselines in both accuracy and robustness across diverse scenarios.

cs.IR↗

arXiv2025

Personalized Federated Recommendation With Knowledge Guidance

Federated Recommendation (FedRec) has emerged as a key paradigm for building privacy-preserving recommender systems. However, existing FedRec models face a critical dilemma: memory-efficient single-knowledge models suffer from a suboptimal knowledge replacement practice that discards valuable personalization, while high-performance dual-knowledge models are often too memory-intensive for practical on-device deployment. We propose Federated Recommendation with Knowledge Guidance (FedRKG), a model-agnostic framework that resolves this dilemma. The core principle, Knowledge Guidance, avoids full replacement and instead fuses global knowledge into preserved local embeddings, attaining the personalization benefits of dual-knowledge within a single-knowledge memory footprint. Furthermore, we introduce Adaptive Guidance, a fine-grained mechanism that dynamically modulates the intensity of this guidance for each user-item interaction, overcoming the limitations of static fusion methods. Extensive experiments on benchmark datasets demonstrate that FedRKG significantly outperforms state-of-the-art methods, validating the effectiveness of our approach. The code is available at https://github.com/Jaehyung-Lim/fedrkg.

cs.IR↗

arXiv2025

Federated Continual Recommendation

The increasing emphasis on privacy in recommendation systems has led to the adoption of Federated Learning (FL) as a privacy-preserving solution, enabling collaborative training without sharing user data. While Federated Recommendation (FedRec) effectively protects privacy, existing methods struggle with non-stationary data streams, failing to maintain consistent recommendation quality over time. On the other hand, Continual Learning Recommendation (CLRec) methods address evolving user preferences but typically assume centralized data access, making them incompatible with FL constraints. To bridge this gap, we introduce Federated Continual Recommendation (FCRec), a novel task that integrates FedRec and CLRec, requiring models to learn from streaming data while preserving privacy. As a solution, we propose F3CRec, a framework designed to balance knowledge retention and adaptation under the strict constraints of FCRec. F3CRec introduces two key components: Adaptive Replay Memory on the client side, which selectively retains past preferences based on user-specific shifts, and Item-wise Temporal Mean on the server side, which integrates new knowledge while preserving prior information. Extensive experiments demonstrate that F3CRec outperforms existing approaches in maintaining recommendation quality over time in a federated environment.

cs.LG↗

arXiv2021

Pseudo-Haptic Button for Improving User Experience of Mid-Air Interaction in VR

Mid-air interaction is one of the promising interaction modalities in virtual reality (VR) due to its merits in naturalness and intuitiveness, but the interaction suffers from the lack of haptic feedback as no force or vibrotactile feedback can be provided in mid-air. As a breakthrough to compensate for this insufficiency, the application of pseudo-haptic features which create the visuo-haptic illusion without actual physical haptic stimulus can be explored. Therefore, this study aimed to investigate the effect of four pseudo-haptic features: proximity feedback, protrusion, hit effect, and penetration blocking on user experience for free-hand mid-air button interaction in VR. We conducted a user study on 21 young subjects to collect user ratings on various aspects of user experience while users were freely interacting with 16 buttons with different combinations of four features. Results indicated that all investigated features significantly improved user experience in terms of haptic illusion, embodiment, sense of reality, spatiotemporal perception, satisfaction, and hedonic quality. In addition, protrusion and hit effect were more beneficial in comparison with the other two features. It is recommended to utilize the four proposed pseudo-haptic features in 3D user interfaces (UIs) to make users feel more pleased and amused, but caution is needed when using proximity feedback together with other features. The findings of this study could be helpful for VR developers and UI designers in providing better interactive buttons in the 3D interfaces.

cs.HC↗

arXiv2021

ViewfinderVR: Configurable Viewfinder for Selection of Distant Objects in VR

Selection is one of the fundamental user interactions in virtual reality (VR) and 3D user interaction, and raycasting has been one of the most popular object selection techniques in VR. However, the selection of small or distant objects through raycasting has been known to be difficult. To overcome this limitation, this study proposed a new technique called ViewfinderVR for improved selection of distant objects in VR, utilizing a virtual viewfinder panel with a modern adaptation of the through-the-lens metaphor. ViewfinderVR enables faster and more accurate target selection by allowing customization of the interaction space projected onto a virtual panel within reach, and users can select objects reflected on the panel with either ray-based or touch interaction. Experimental results of Fitts' law-based tests with 20 participants showed that ViewfinderVR outperformed traditional raycasting in terms of task performance (movement time, error rate, and throughput) and perceived workload (NASA-TLX ratings), where touch interaction was superior to ray-based interaction. The associated user behavior was also recorded and analyzed to understand the underlying reasons for the improved task performance and reduced workload. The proposed technique can be used in VR applications to enhance the selection of distant objects.

cs.HC↗