分析124篇推荐系统解释评估研究，发现用户特征影响解释效果且现有研究代表性不足。
Whom do Explanations Serve? A Systematic Literature Survey of User Characteristics in Explainable Recommender Systems Evaluation
- 系统调研124篇用户研究，分析用户特征对解释效果的影响机制
- 多数研究仅覆盖特定用户群体，结论难以推广至真实场景
- 指出数据报告不一致问题，呼吁提升评估的包容性与可复现性
为推荐系统添加解释被认为能提升用户信任与系统透明度。尽管其他领域研究表明用户特征会影响对解释的感知，但推荐系统领域的评估却很少考虑此类因素。本文系统调研了124篇包含用户研究的推荐系统解释评估论文，分析其参与者描述及结果，发现多数研究仅针对特定用户群体，未能代表实际应用场景中的用户。这严重限制了当前研究成果的泛化能力。此外，数据报告存在不一致现象，影响结果可复现性。因此，本文建议采取措施推动更包容、可复现的评估方法。
原文摘要 · Abstract (English)
Adding explanations to recommender systems is said to have multiple benefits, such as increasing user trust or system transparency. Previous work from other application areas suggests that specific user characteristics impact the users' perception of the explanation. However, we rarely find this type of evaluation for recommender systems explanations. This paper addresses this gap by surveying 124 papers in which recommender systems explanations were evaluated in user studies. We analyzed their participant descriptions and study results where the impact of user characteristics on the explanation effects was measured. Our findings suggest that the results from the surveyed studies predominantly cover specific users who do not necessarily represent the users of recommender systems in the evaluation domain. This may seriously hamper the generalizability of any insights we may gain from current studies on explanations in recommender systems. We further find inconsistencies in the data reporting, which impacts the reproducibility of the reported results. Hence, we recommend actions to move toward a more inclusive and reproducible evaluation.
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