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.

翻译：物品推荐（预测用户在推荐系统中可能与目录中新物品交互的任务）和链接预测（识别知识图谱中缺失链接的任务）长期以来被视为不同的问题。在本研究中，我们表明物品推荐问题可视为链接预测问题的一个实例，其中图中的实体代表用户和物品，任务在于预测关系类型<<interactsWith>>的缺失实例。为初步验证该假设，我们决定在物品推荐任务上测试三种流行的基于分解的链接预测模型，结果表明其预测准确性与十种最先进的推荐模型具有竞争力。此举旨在展示前者如何无需对其架构进行任何特定修改，即可无缝且有效地应用于推荐任务。最后，在开始揭示所选链接预测模型推荐性能背后关键原因的同时，我们探索了其超参数值的不同设置，为未来研究方向铺平道路。