AI 中文总结
针对推荐系统研究中数据集选择缺乏系统工具的问题，提出FINALLY数据集推荐系统，通过多种策略和配置生成满足约束的数据集集合，实验验证了其技术一致性与策略有效性。
AI 中文摘要
数据集的选择决定了推荐系统算法在经验评估中的条件，然而现有工具在构建能够同时满足实验约束和集合级选择目标的完整数据集集合方面支持有限。为解决这一问题，我开发了FINALLY，一个基于网络的数据集推荐系统，用于为离线推荐系统评估构建可配置的数据集集合。FINALLY结合了必需数据集、候选池限制、元数据过滤器、可配置的目标集合大小、随机选择，以及基于改进的有效协方差和凸包目标函数的多样性与非多样性策略。我通过十种系统化变化的配置下的420次推荐运行对FINALLY进行了评估。所有被评估的数据集集合均满足了适用的目标大小、去重、快照成员资格、必需数据集和元数据过滤要求。所有40种确定性策略-配置组合均可复现。基于有效协方差和基于凸包的策略在所有十种配置中均产生了预期的多样性-非多样性得分排序。在其相应目标下，多样性策略产生的得分高于所有30个特定配置的随机结果，而非多样性策略产生的得分低于所有30个随机结果。这些结果确立了所评估的FINALLY工作流的技术一致性，并表明所实施的策略在调查的配置空间内遵循了其预期的优化方向。它们并未确立所生成选择的科学适用性、全局最优性或实际优越性。
英文摘要
Dataset selection shapes the empirical conditions under which recommender-system algorithms are evaluated, yet existing tools provide limited support for constructing complete dataset sets that jointly satisfy experimental constraints and set-level selection objectives. To address this problem, I developed FINALLY, a web-based dataset recommender for constructing configurable dataset sets for offline recommender-systems evaluations. FINALLY combines required datasets, candidate-pool restrictions, metadata filters, configurable target-set sizes, Random selection, and diverse and non-diverse strategies based on adapted Effective Covariance and Convex Hull objectives. I evaluated FINALLY through 420 recommendation runs across ten systematically varied configurations. All evaluated dataset sets satisfied the applicable target-size, duplicate-avoidance, snapshot-membership, required-dataset, and metadata-filter requirements. All 40 deterministic strategy--configuration combinations were reproducible. Both the Effective-Covariance-based and Convex-Hull-based strategies produced the expected diverse-versus-non-diverse score ordering in all ten configurations. Under their corresponding objectives, the diverse strategies produced scores above all 30 configuration-specific Random results, whereas the non-diverse strategies produced scores below all 30 Random results. These results establish technical consistency for the evaluated FINALLY workflow and show that the implemented strategies follow their intended optimization directions within the investigated configuration space. They do not establish the scientific suitability, global optimality, or practical superiority of the generated selections.
CommentsBachelor's thesis, University of Siegen, 2026
