arXiv subjects

Chaohua Yang

Publications and source records attributed to Chaohua Yang.

4 recordsLinked to original sources

arXiv2026

DuTri: Dual-Branch Modeling with Triadic Factorization for Multimodal Multi-Scenario Recommendation

As recommendation scenarios become increasingly diverse and multimodal information grows more prevalent, effectively modeling cross-scenario user preferences and multimodal semantics has become a critical challenge in recommender systems. When incorporating multimodal information, existing multi-scenario recommendation (MSR) methods typically treat textual and visual representations as generic input features and directly feed them into their original parameter-sharing architectures. However, this extension may not fully accommodate the different modeling requirements of collaborative and multimodal information and may not be the best modeling paradigm. Our controlled experiments show that directly adopting these architectures is not the optimal modeling approach. To this end, we propose Dual-Branch Modeling with Triadic Factorization (DuTri), a framework for multimodal multi-scenario recommendation comprising a collaborative branch and a multimodal branch. Specifically, the collaborative branch learns structured collaborative preference representations through CANDECOMP/PARAFAC (CP) decomposition of user-scenario-item interactions and residual quantization, thereby capturing cross-scenario commonalities and scenario-specific differences. The multimodal branch leverages the learned user and scenario preferences to guide adaptive modality fusion; additionally, item-level contrastive learning aligns collaborative item preference representations with multimodal representations to facilitate effective collaboration between the two branches. Extensive experiments on two real-world datasets, AntM$^2$C and Amazon, demonstrate that DuTri outperforms existing multimodal and multi-scenario recommendation baselines in overall recommendation performance, validating the effectiveness of the proposed framework.

cs.IR↗

arXiv2026

Towards a Unified Framework of Optimal Input-Side Dimensional Configuration for Deep CTR Prediction

Deep click-through rate (CTR) prediction has achieved substantial success through increasingly expressive feature interaction architectures. However, predictive performance also depends on how input-side information is configured before interaction modeling. Existing studies have shown that selecting informative samples, features, or embedding units can improve prediction accuracy. Nevertheless, they primarily optimize these selection mechanisms independently along a single input-side dimension, overlooking their shared basic form and the inherent dependencies across input levels, thereby limiting the effectiveness of input-side dimensional configuration. To address this issue, we propose OptIDC, a unified framework for Optimal Input-side Dimensional Configuration in deep CTR prediction. OptIDC jointly learns sample, feature, and embedding-unit level configurations within a unified framework by integrating their corresponding selection mechanisms. To explicitly coordinate selection decisions across different levels, OptIDC further introduces a selection-weighted Optimal Transport mechanism that aligns coarse- and fine-grained selections via transport over finite measures, thereby alleviating cross-level selection conflicts while preserving the semantics of each input level. Moreover, OptIDC is model-agnostic and can be seamlessly integrated into existing CTR models. Extensive experiments on four real-world benchmark datasets demonstrate that OptIDC consistently achieves higher AUC than existing input-side selection methods, validating the effectiveness of unified input-side dimensional configuration.

cs.IR↗

arXiv2026

VeruSteer: Learning What to Repair Next in Verus

LLM-based verification systems repair Rust proofs by revising programs in response to Verus diagnostics. When several error types are reported, pipelines such as AutoVerus and VeruSAGE select the next repair target using a preset priority. Yet each repair changes the program and the errors that follow, so the same reported types can require different choices at different states. We present VeruSteer, which learns to select error types from the outcomes of alternative repair trajectories. It constructs repair trees offline and compares paths from the same initial program by verification success, repair rounds, and final verification progress. These comparisons supervise a selector that combines step-wise imitation with trajectory preference ranking. The selector uses self-attention over the program, verifier feedback, and earlier selections to rank the currently reported types. It replaces the default priority within a single repair trajectory, keeping the existing repair agents and adding no repair or verifier calls for selection. We evaluate VeruSteer by replaying its choices on stored AutoVerus and VeruSAGE test trees under four generation configurations. Across 1,270 test trees, VeruSteer increases verified outcomes from 185 to 194 (14.57\% to 15.28\%), gaining 12 successes and losing three for a net improvement of 0.71 percentage points. Among trees where the default fails but a recorded alternative verifies, VeruSteer succeeds on 3 of 4 AutoVerus trees and 9 of 11 VeruSAGE trees. Among trees with recorded alternatives where the default verifies, it retains success on 14 of 15 AutoVerus trees and 12 of 14 VeruSAGE trees. It also finds shorter successful trajectories and improves final progress on some trees where all recorded trajectories fail.

cs.SE↗

arXiv2025

Automated Information Flow Selection for Multi-scenario Multi-task Recommendation

Multi-scenario multi-task recommendation (MSMTR) systems must address recommendation demands across diverse scenarios while simultaneously optimizing multiple objectives, such as click-through rate and conversion rate. Existing MSMTR models typically consist of four information units: scenario-shared, scenario-specific, task-shared, and task-specific networks. These units interact to generate four types of relationship information flows, directed from scenario-shared or scenario-specific networks to task-shared or task-specific networks. However, these models face two main limitations: 1) They often rely on complex architectures, such as mixture-of-experts (MoE) networks, which increase the complexity of information fusion, model size, and training cost. 2) They extract all available information flows without filtering out irrelevant or even harmful content, introducing potential noise. Regarding these challenges, we propose a lightweight Automated Information Flow Selection (AutoIFS) framework for MSMTR. To tackle the first issue, AutoIFS incorporates low-rank adaptation (LoRA) to decouple the four information units, enabling more flexible and efficient information fusion with minimal parameter overhead. To address the second issue, AutoIFS introduces an information flow selection network that automatically filters out invalid scenario-task information flows based on model performance feedback. It employs a simple yet effective pruning function to eliminate useless information flows, thereby enhancing the impact of key relationships and improving model performance. Finally, we evaluate AutoIFS and confirm its effectiveness through extensive experiments on two public benchmark datasets and an online A/B test.

cs.IR↗