Reasoning on knowledge graph (KG) has been studied for explainable recommendation due to it's ability of providing explicit explanations. However, current KG-based explainable recommendation methods unfortunately ignore the temporal information (such as purchase time, recommend time, etc.), which may result in unsuitable explanations. In this work, we propose a novel Time-aware Path reasoning for Recommendation (TPRec for short) method, which leverages the potential of temporal information to offer better recommendation with plausible explanations. First, we present an efficient time-aware interaction relation extraction component to construct collaborative knowledge graph with time-aware interactions (TCKG for short), and then introduce a novel time-aware path reasoning method for recommendation. We conduct extensive experiments on three real-world datasets. The results demonstrate that the proposed TPRec could successfully employ TCKG to achieve substantial gains and improve the quality of explainable recommendation.
翻译:由于知识图(KG)具有提供明确解释的能力,因此对基于知识图(KG)的理论进行了研究,以便提出可以解释的建议。然而,目前基于KG的可解释建议方法不幸忽略了时间信息(例如购买时间、建议时间等),这可能会导致不适当的解释。在这项工作中,我们为建议(短期TPRec)方法提出了一个新的时间认知路径推理方法,利用时间信息的潜力提出更好的建议,并作出合理的解释。首先,我们提出了一个高效的时间认知互动提取要素,以构建与时间认知互动的合作知识图(短期为TCKG),然后为建议引入新的有时间认知路径推理方法。我们在三个真实世界数据集上进行了广泛的实验。结果表明,拟议的TPRec可以成功地利用TCKG实现重大收益,提高可解释建议的质量。