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This paper addresses the challenge of data sparsity in recommender systems that rely on user-generated content (UGC) by introducing MOSAIC, a method that constructs meta-reviews for users by aggregating sentiment evidence from neighboring users' reviews. By employing a multi-gate mixture-of-experts architecture, MOSAIC optimizes both rating predictions and attribute-sentiment predictions, while an attention module personalizes the output for each user. Experimental results across four datasets show that MOSAIC significantly enhances recommendation accuracy and explanation quality, particularly benefiting users with limited interaction history.
Aggregating insights from neighboring users' reviews can transform how we tackle data sparsity in recommender systems, leading to better predictions and richer explanations.
Data sparsity remains a long-standing challenge in recommender systems, and it becomes more severe for methods relying on user-generated content (UGC) such as textual reviews, which capture fine-grained preferences but require more user efforts to produce. As a result, UGC exhibits (1) missing reviews, where interactions lack any review, and (2) incomplete reviews, where available reviews cover only a subset of relevant attributes. Existing approaches often overlook these UGC-specific issues, leading to degraded accuracy. Motivated by meta-review in academic peer review, we propose MOSAIC (Meta-review On Sparse And Incomplete user-generated Content), which constructs a meta-review for each target user by aggregating attribute-sentiment evidence from neighbor users'reviews. A multi-gate mixture-of-experts (MMoE) architecture jointly optimizes rating prediction and meta-review attribute-sentiment prediction, while an attention module personalizes the aggregated meta-review signals to each target user, yielding both refined rating predictions and attribute-level explanations. Experiments on four real-world datasets demonstrate that MOSAIC consistently outperforms state-of-the-art baselines in both recommendation accuracy and explanation quality, mitigating UGC sparsity and incompleteness while delivering consistent gains for users with limited interaction history.
