Recommendation systems suffer in the strict cold-start (SCS) scenario, where the user-item interactions are entirely unavailable. The ID-based approaches completely fail to work. Cold-start recommenders, on the other hand, leverage item contents to map the new items to the existing ones. However, the existing SCS recommenders explore item contents in coarse-grained manners that introduce noise or information loss. Moreover, informative data sources other than item contents, such as users' purchase sequences and review texts, are ignored. We explore the role of the fine-grained item attributes in bridging the gaps between the existing and the SCS items and pre-train a knowledgeable item-attribute graph for SCS item recommendation. Our proposed framework, ColdGPT, models item-attribute correlations into an item-attribute graph by extracting fine-grained attributes from item contents. ColdGPT then transfers knowledge into the item-attribute graph from various available data sources, i.e., item contents, historical purchase sequences, and review texts of the existing items, via multi-task learning. To facilitate the positive transfer, ColdGPT designs submodules according to the natural forms of the data sources and coordinates the multiple pre-training tasks via unified alignment-and-uniformity losses. Our pre-trained item-attribute graph acts as an implicit, extendable item embedding matrix, which enables the SCS item embeddings to be easily acquired by inserting these items and propagating their attributes' embeddings. We carefully process three public datasets, i.e., Yelp, Amazon-home, and Amazon-sports, to guarantee the SCS setting for evaluation. Extensive experiments show that ColdGPT consistently outperforms the existing SCS recommenders by large margins and even surpasses models that are pre-trained on 75-224 times more, cross-domain data on two out of four datasets.

翻译：推荐系统在严格冷启动场景中面临困境，此时用户-物品交互完全不可用，基于ID的方法完全失效。相较之下，冷启动推荐器利用物品内容将新物品与现有物品关联起来。然而，现有SCS推荐器以粗粒度方式探索物品内容，导致噪声或信息丢失。此外，除物品内容外，如用户购买序列和评论文本等信息源也被忽略。我们探索了细粒度物品属性在弥合现有物品与SCS物品之间差距中的作用，并为SCS物品推荐预训练了一个知识丰富的物品-属性图。我们提出的框架ColdGPT通过从物品内容中提取细粒度属性，将物品-属性关联建模为物品-属性图。随后，ColdGPT通过多任务学习，从各种可用数据源（即现有物品的物品内容、历史购买序列和评论文本）中将知识迁移到物品-属性图中。为促进正向迁移，ColdGPT根据数据源的自然形态设计了子模块，并通过统一的对齐-均匀性损失协调多个预训练任务。我们预训练的物品-属性图作为隐式可扩展的物品嵌入矩阵，通过插入新物品并传播其属性嵌入，可轻松获取SCS物品的嵌入。我们精心处理了三个公开数据集（Yelp、Amazon-home和Amazon-sports），以确保评估符合SCS设定。大量实验表明，ColdGPT大幅且持续优于现有SCS推荐器，甚至在四个数据集中的两个上超越了基于75-224倍跨领域数据进行预训练的模型。