This paper examines the challenging problem of new user cold starts in subset labelled and extremely sparsely labelled big data. We introduce a new Isle of Wight Supply Chain (IWSC) dataset demonstrating these characteristics. We also introduce a new technique addressing these challenges, the Transitive Semantic Relationships (TSR) model, which infers potential relationships from user and item text content and few labelled examples. We perform both implicit and explicit evaluation of TSR as a recommender system and from new user cold starts we achieve a hit-rate@10 of 77% on a collection of 630 items with only 376 supply-chain consumer labels, and 67% with only 142 supply-chain supplier labels, demonstrating a high level of performance even with extremely few labels in challenging cold-start scenarios. TSR is suitable for any dataset featuring few labels and user and item content, where similarity of content indicates similar relationship forming capability. TSR can be used as a standalone recommender system or to complement existing high-performance recommender models that require more labels or do not support cold starts.

中文翻译：

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大数据冷启动的建议

本文研究了在子集标记和极稀疏标记的大数据中新用户冷启动的挑战性问题。我们引入了一个新的怀特岛供应链 (IWSC) 数据集，展示了这些特征。我们还引入了一种新技术来解决这些挑战，即传递语义关系 (TSR) 模型，该模型从用户和项目文本内容以及少量标记示例中推断出潜在关系。我们对作为推荐系统的 TSR 进行隐式和显式评估，从新用户冷启动开始，我们在仅包含 376 个供应链消费者标签的 630 个项目的集合上实现了 77% 的命中率@10，只有 67% 142 个供应链供应商标签，即使在具有挑战性的冷启动场景中标签极少，也能展现出高水平的性能。TSR 适用于任何标签较少、用户和项目内容较少的数据集，其中内容的相似性表示具有相似的关系形成能力。TSR 可以用作独立的推荐系统或补充现有的需要更多标签或不支持冷启动的高性能推荐模型。