A real-world recommender usually adopts heterogeneous types of user feedbacks, for example, numerical ratings such as 5-star grades and binary ratings such as likes and dislikes. In this work, we focus on transferring knowledge from binary ratings to numerical ratings, facing a more serious data sparsity problem. Conventional Collective Factorization methods usually assume that there are shared user and item latent factors across multiple related domains, but may ignore the shared common knowledge of rating patterns. Furthermore, existing works may also fail to consider the hierarchical structures in the heterogeneous recommendation scenario (i.e., genre, sub-genre, detailed-category). To address these challenges, in this paper, we propose a novel Deep Low-rank Sparse Collective Factorization (DLSCF) framework for heterogeneous recommendation. Specifically, we adopt low-rank sparse decomposition to capture the common rating patterns in related domains while splitting the domain-specific patterns. We also factorize the model in multiple layers to capture the affiliation relation between latent categories and sub-categories. We propose both batch and Stochastic Gradient Descent (SGD) based optimization algorithms for solving DLSCF. Experimental results on MoviePilot, Netfilx, Flixter, MovieLens10M and MovieLens20M datasets demonstrate the effectiveness of the proposed algorithms, by comparing them with several state-of-the-art batch and SGD based approaches.

中文翻译：

---

通过深度低秩稀疏集体分解的异构推荐


现实世界的推荐器通常采用异构类型的用户反馈，例如，诸如五星级之类的数字评级和诸如喜欢和不喜欢之类的二元评级。在这项工作中，我们专注于将知识从二进制评级转移到数字评级，面临更严重的数据稀疏问题。传统的集体分解方法通常假设多个相关领域之间存在共享的用户和项目潜在因素，但可能忽略评级模式的共享常识。此外，现有的工作也可能未能考虑异构推荐场景中的层次结构（即流派、子流派、详细类别）。为了应对这些挑战，在本文中，我们提出了一种新颖的用于异构推荐的深度低秩稀疏集体分解（DLSCF）框架。具体来说，我们采用低秩稀疏分解来捕获相关领域中的常见评分模式，同时分割特定领域的模式。我们还将模型分解为多个层，以捕获潜在类别和子类别之间的从属关系。我们提出了基于批量和随机梯度下降（SGD）的优化算法来求解 DLSCF。 MoviePilot、Netfilx、Flixter、MovieLens10M 和 MovieLens20M 数据集上的实验结果通过与几种最先进的批处理和基于 SGD 的方法进行比较，证明了所提出算法的有效性。