把推荐系统当作链接预测，用通用模型就能高效完成。
Dot Product is All You Need: Bridging the Gap Between Item Recommendation and Link Prediction
- 将用户-物品交互建模为图中缺失的<<interactsWith>>关系
- 三种经典链接预测模型在推荐任务上表现媲美顶尖推荐算法
- 无需修改结构，直接迁移模型适用于推荐场景
物品推荐（预测用户是否会与新物品互动）和链接预测（识别知识图谱中的缺失链接）长期被视为不同问题。本文表明，物品推荐可视为链接预测的一种实例：图中节点代表用户和物品，目标是预测<<interactsWith>>关系的缺失实例。我们初步测试了三种主流因子分解型链接预测模型在推荐任务上的表现，结果发现其预测精度可与十种最先进的推荐模型相媲美。这证明这些模型无需任何架构修改即可无缝、高效地应用于推荐任务。最后，我们探索了不同超参数设置对性能的影响，为未来研究方向提供基础。
原文摘要 · Abstract (English)
Item recommendation (the task of predicting if a user may interact with new items from the catalogue in a recommendation system) and link prediction (the task of identifying missing links in a knowledge graph) have long been regarded as distinct problems. In this work, we show that the item recommendation problem can be seen as an instance of the link prediction problem, where entities in the graph represent users and items, and the task consists of predicting missing instances of the relation type <<interactsWith>>. In a preliminary attempt to demonstrate the assumption, we decide to test three popular factorisation-based link prediction models on the item recommendation task, showing that their predictive accuracy is competitive with ten state-of-the-art recommendation models. The purpose is to show how the former may be seamlessly and effectively applied to the recommendation task without any specific modification to their architectures. Finally, while beginning to unveil the key reasons behind the recommendation performance of the selected link prediction models, we explore different settings for their hyper-parameter values, paving the way for future directions.
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