arXiv subjects

Song-Duo Ma

Publications and source records attributed to Song-Duo Ma.

5 recordsLinked to original sources

arXiv2026

Are We Really Making Progress in Group Recommendation? Unmasking the Tie-Breaking Illusion

Recent group recommendation methods have reported strong improvements on standard benchmarks, but it remains unclear whether these gains always reflect genuine advances in modeling group preferences. In this paper, we show that several recent methods are affected by a systematic evaluation bias caused by the interaction between training-time score compression and evaluation-time deterministic tie-breaking. Specifically, an additional sigmoid transformation before the BPR objective can greatly increase tied top scores, making top-K metrics such as HR@K and NDCG@K highly sensitive to how ties are resolved. We revisit recent representative methods and their baselines on CAMRa2011 and Mafengwo under both group and user recommendation settings, and evaluate them with a tie-aware protocol that computes the exact expectation of HR@K and NDCG@K under uniform random tie-breaking. Our results show that many previously reported improvements shrink substantially under tie-aware evaluation, and the relative ranking of methods can change markedly. We further show that the additional sigmoid may act as implicit margin smoothing during optimization, and that temperature-scaled BPR can retain much of this benefit without inducing severe tie inflation. Overall, our findings highlight the importance of tie-aware evaluation for establishing reliable progress in group recommendation. The code is available at https://github.com/songduoma/TieAwareGroupRec.

cs.IR↗

arXiv2026

Structure-Preserving Projection for Mitigating Modality Bias in LLM-Based Sequential Recommendation

Recent LLM-based recommenders integrate textual and collaborative signals by projecting collaborative embeddings into the embedding space of the LLM. However, this projection can introduce modality bias that distorts the underlying collaborative structure and limits the usefulness of projected embeddings. To address this issue, we propose a novel structure-preserving projection approach that maintains the relational geometry of collaborative embeddings through dedicated structure-preserving losses. Comprehensive experiments demonstrate that our approach consistently improves recommendation performance, providing a more reliable path for LLM-based recommendation.

cs.IR↗

arXiv2026

Rethinking Fairness in LLM-Based Recommender Systems: A Survey

Large Language Models (LLMs) are reshaping recommender systems by enabling more semantic, generative, and interactive recommendation pipelines. However, this shift also introduces new fairness challenges, as biases may arise from pretrained knowledge, prompts, generated explanations, decoding strategies, and feedback loops. This survey provides a systematic review of fairness in LLM-based recommender systems (LLM4Rec), organizing existing studies through a two-dimensional view of bias mechanisms and fairness targets, together with a structured overview of the evaluation landscape and mitigation strategies. We further connect fairness with broader trustworthy concerns, including explainability, privacy, robustness, and controllability. To the best of our knowledge, this is the first survey specifically focused on fairness in LLM4Rec, aiming to provide a structured foundation for future research on comprehensive and reliable fairness evaluation in LLM4Rec.

cs.IR↗

arXiv2026

Beyond Uniform Token Training: A Multi-Target Framework for Learning Token-Weighted Objectives in Generative Recommenders

Recent generative recommendation models recast next-item prediction as the generation of a semantic identifier sequence. While this formulation enables autoregressive models to produce item IDs directly, the commonly used token-level likelihood objective does not distinguish between tokens that play different roles in item identification. This limitation is especially pronounced for semantic-ID representations, where prefix tokens often determine coarse item groups and later tokens provide finer-grained disambiguation. To better align training with the structure of semantic IDs, we study token-level learning signals from two complementary perspectives. First, we introduce a prefix-aware weighting scheme, Front-Greater Weighting, which emphasizes tokens according to their contribution to reducing semantic ambiguity among candidate items. Second, frequency weighting increases the learning emphasis on infrequent tokens, addressing the long-tailed distributions and popularity bias commonly observed in recommendation data. We further introduce a multi-target optimization framework with curriculum learning, which integrates the two token-weighted objectives with the standard likelihood and enables stable optimization with adaptive emphasis across training stages. Experiments on multiple benchmark datasets demonstrate that the proposed approach consistently improves generative recommendation performance over strong baselines and prior token-weighting methods. Additional analyses show that the method is robust across different semantic-ID constructions and backbone scales, and that it improves recommendation quality for both popular and long-tail items. Code is available at github repository.

cs.IR↗

arXiv2026

RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Robust Fake News Detection

To efficiently combat the spread of LLM-generated misinformation, we present RADAR, a Retrieval-Augmented Detector with Adversarial Refinement for robust fake news detection. Our approach employs a generator that rewrites real articles with factual perturbations, paired with a lightweight detector that verifies claims using dense passage retrieval. To enable effective co-evolution, we introduce verbal adversarial feedback (VAF). Rather than relying on scalar rewards, VAF issues structured natural-language critiques; these guide the generator toward more sophisticated evasion attempts, compelling the detector to adapt and improve. On a fake news detection benchmark, RADAR consistently outperforms strong retrieval-augmented trainable baselines, as well as general-purpose LLMs with retrieval. Further analysis shows that detector-side retrieval yields the largest gains, while VAF and few-shot demonstrations provide complementary benefits. RADAR also transfers better to fake news generated by an unseen external attacker, indicating improved robustness beyond the co-evolved training setting.

cs.CL↗