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cs.IR2025
Towards Bridging Review Sparsity in Recommendation with Textual Edge Graph Representation
Leyao Wang, Xutao Mao, Xuhui Zhan +5
Textual reviews enrich recommender systems with fine-grained preference signals and enhanced explainability. However, in real-world scenarios, users rarely leave reviews, resulting…
cs.IR2025
SimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data Augmentation
Yuying Zhao, Xiaodong Yang, Huiyuan Chen +4
Deep Neural Networks (DNNs) are extensively used in collaborative filtering due to their impressive effectiveness. These systems depend on interaction data to learn user and item e…