为推荐系统研究提供可配置的数据集组合推荐工具。
FINALLY: A Dataset Recommender System for Recommender-Systems Research
- 基于多种约束条件与优化目标，自动推荐数据集组合。
- 在10种配置下均满足大小、去重、元数据等要求，结果可复现。
- 适合需要规范数据集选择的推荐系统实验者使用。
数据集选择决定了推荐系统算法评估的实验条件，但现有工具难以支持构建同时满足实验约束和整体选择目标的完整数据集集合。为此，本文开发了FINALLY——一个基于网页的数据集推荐系统，用于构建可配置的数据集组合，以支持离线推荐系统评估。FINALLY结合了必需数据集、候选池限制、元数据过滤、可配置的目标集合规模，以及基于改进的有效协方差和凸包目标的随机、多样化与非多样化策略。通过在十种系统性变化的配置下进行420次推荐运行评估，所有数据集组合均满足目标规模、去重、快照成员、必需数据集和元数据过滤要求。全部40种确定性策略-配置组合均可复现。基于有效协方差和凸包的目标策略在所有十种配置中均实现了预期的多样化与非多样化得分排序。在各自目标下，多样化策略得分高于所有30个配置相关的随机结果，非多样化策略得分低于所有30个随机结果。这些结果验证了FINALLY工作流的技术一致性，表明所实现策略在其研究配置空间内遵循了预期优化方向。但未证明生成选择的科学适用性、全局最优性或实际优越性。
原文摘要 · Abstract (English)
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.
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