arXiv:2611.13972 · v1
Data Quality Challenges in Multimodal Tourism Recommender Systems
摘要
Modern recommender systems increasingly consider diverse data modalities to enhance model performance. However, the quality of the underlying multimodal data (e.g. data from social media platforms) has received limited attention so far. Focusing on tourism recommendation, we examine real-world challenges encountered when using datasets from Yelp and Instagram. Issues include noise from fake reviews, hallucinated content from LLMs, modality contradictions, and demographic bias. We outline future directions such as quality-aware fusion, structured and controlled LLM-based content generation to reduce hallucinations, and user group differentiation to improve the robustness and reliability of multimodal recommendation systems.
作者备注：4 pages. Presented at the DaQuaMRec Workshop, co-located with ACM RecSys 2025
期刊信息：CEUR Workshop Proceedings, Vol. 4188, pp. 20-23, 2026