Cold-start rating prediction is a fundamental problem in recommender systems that has been extensively studied. Many methods have been proposed that exploit explicit relations among existing data, such as collaborative filtering, social recommendations and heterogeneous information network, to alleviate the data insufficiency issue for cold-start users and items. However, the explicit relations constructed based on data between different roles may be unreliable and irrelevant, which limits the performance ceiling of the specific recommendation task. Motivated by this, in this paper, we propose a flexible framework dubbed heterogeneous interaction rating network (HIRE). HIRE dose not solely rely on the pre-defined interaction pattern or the manually constructed heterogeneous information network. Instead, we devise a Heterogeneous Interaction Module (HIM) to jointly model the heterogeneous interactions and directly infer the important interactions via the observed data. In the experiments, we evaluate our model under three cold-start settings on three real-world datasets. The experimental results show that HIRE outperforms other baselines by a large margin. Furthermore, we visualize the inferred interactions of HIRE to confirm the contribution of our model.

翻译：冷启动评分预测是推荐系统中一个被广泛研究的基础性问题。为缓解冷启动用户与物品的数据稀疏问题，已有诸多方法被提出，这些方法利用现有数据间的显式关系，如协同过滤、社交推荐及异构信息网络等。然而，基于不同角色间数据构建的显式关系可能不可靠或不相关，从而限制了特定推荐任务的性能上限。受此启发，本文提出一个灵活的框架，称为异构交互评分网络（HIRE）。HIRE不依赖于预定义的交互模式或人工构建的异构信息网络。相反，我们设计了一个异构交互模块（HIM），以联合建模异构交互，并通过观测数据直接推断重要的交互关系。在实验中，我们在三个真实数据集上，针对三种冷启动场景评估了所提模型。实验结果表明，HIRE显著优于其他基线方法。此外，我们对HIRE推断出的交互关系进行了可视化，以验证模型的有效性。