Existing recommender systems face difficulties with zero-shot items, i.e. items that have no historical interactions with users during the training stage. Though recent works extract universal item representation via pre-trained language models (PLMs), they ignore the crucial item relationships. This paper presents a novel paradigm for the Zero-Shot Item-based Recommendation (ZSIR) task, which pre-trains a model on product knowledge graph (PKG) to refine the item features from PLMs. We identify three challenges for pre-training PKG, which are multi-type relations in PKG, semantic divergence between item generic information and relations and domain discrepancy from PKG to downstream ZSIR task. We address the challenges by proposing four pre-training tasks and novel task-oriented adaptation (ToA) layers. Moreover, this paper discusses how to fine-tune the model on new recommendation task such that the ToA layers are adapted to ZSIR task. Comprehensive experiments on 18 markets dataset are conducted to verify the effectiveness of the proposed model in both knowledge prediction and ZSIR task.

翻译：现有推荐系统在面对零样本物品（即在训练阶段与用户无历史交互的物品）时存在困难。尽管近期研究通过预训练语言模型（PLMs）提取通用物品表征，但忽略了关键的物品关系。本文针对零样本基于物品的推荐（ZSIR）任务提出一种新范式，该方法在产品知识图谱（PKG）上预训练模型，以优化来自PLMs的物品特征。我们识别出PKG预训练面临的三个挑战：PKG中的多类型关系、物品通用信息与关系之间的语义差异，以及从PKG到下游ZSIR任务的领域差异。通过提出四项预训练任务和新型任务导向适应（ToA）层，我们解决了上述挑战。此外，本文讨论了如何在新的推荐任务上微调模型，使ToA层适应ZSIR任务。在涵盖18个市场的数据集上进行综合实验，验证了所提模型在知识预测和ZSIR任务中的有效性。