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基础且被广泛研究的问题。许多方法通过利用现有数据中的显式关系（如协同过滤、社会推荐和异质信息网络）来缓解冷启动用户和项目的数据稀疏问题。然而，基于不同角色间数据构建的显式关系可能不可靠且不相关，从而限制了具体推荐任务的性能上限。受此启发，本文提出了一种灵活的框架——异质交互评分网络（HIRE）。HIRE并不单纯依赖预定义的交互模式或人工构建的异质信息网络，而是设计了一个异质交互模块（HIM），通过观测数据联合建模异质交互，并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的三种冷启动场景下评估了模型性能。实验结果表明，HIRE以较大优势优于其他基线方法。此外，我们通过可视化HIRE推断的交互关系，进一步验证了模型的贡献。