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。现有许多方法利用显式关联（如协同过滤、社交推荐和异构信息网络）来缓解冷启动用户和物品的数据稀疏问题。然而，基于不同角色数据构建的显式关联可能不可靠或无关，这限制了特定推荐任务的性能上限。基于此，本文提出一个灵活框架——异构交互评分网络（HIRE）。HIRE不单纯依赖预定义的交互模式或人工构建的异构信息网络，而是设计异构交互模块（HIM）来联合建模异构交互，并通过观测数据直接推断重要交互。我们在三个真实数据集上、三种冷启动设置下评估模型，实验结果表明HIRE大幅优于其他基线。此外，我们通过可视化HIRE推断的交互，进一步验证了模型的贡献。