论文标题
带有图侧信息的离散值潜在偏好矩阵估计
Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information
论文作者
论文摘要
将图侧信息纳入推荐系统已被广泛用于更好地预测评分，但是相对较少的作品集中在理论保证上。 Ahn等。 （2018年）首先在存在图侧信息的情况下表征了最佳样本复杂性，但是由于对未知潜在优先矩阵和用户群集的结构进行了严格，不切实际的假设，结果受到限制。在这项工作中，我们提出了一个新模型，其中1）未知的潜在偏好矩阵可以具有任何离散值，2）可以将用户聚集到多个群集中，从而放松先前工作中的假设。在这个新模型下，我们充分表征了最佳样本复杂性，并开发了与最佳样品复杂性相匹配的计算效率算法。我们的算法在建模错误并在合成数据和真实数据上的预测性能方面胜过现有算法。
Incorporating graph side information into recommender systems has been widely used to better predict ratings, but relatively few works have focused on theoretical guarantees. Ahn et al. (2018) firstly characterized the optimal sample complexity in the presence of graph side information, but the results are limited due to strict, unrealistic assumptions made on the unknown latent preference matrix and the structure of user clusters. In this work, we propose a new model in which 1) the unknown latent preference matrix can have any discrete values, and 2) users can be clustered into multiple clusters, thereby relaxing the assumptions made in prior work. Under this new model, we fully characterize the optimal sample complexity and develop a computationally-efficient algorithm that matches the optimal sample complexity. Our algorithm is robust to model errors and outperforms the existing algorithms in terms of prediction performance on both synthetic and real data.
