Following recent successes in exploiting both latent factor and word
embedding models in recommendation, we propose a novel Regularized
Multi-Embedding (RME) based recommendation model that simultaneously
encapsulates the following ideas via decomposition: (1) which items a user
likes, (2) which two users co-like the same items, (3) which two items users
often co-liked, and (4) which two items users often co-disliked. In
experimental validation, the RME outperforms competing state-of-the-art models
in both explicit and implicit feedback datasets, significantly improving
Recall@5 by 5.9~7.0%, NDCG@20 by 4.3~5.6%, and MAP@10 by 7.9~8.9%. In addition,
under the cold-start scenario for users with the lowest number of interactions,
against the competing models, the RME outperforms NDCG@5 by 20.2% and 29.4% in
MovieLens-10M and MovieLens-20M datasets, respectively. Our datasets and source
code are available at: this https URL

提出了一种基于正则化的多嵌入式推荐模型（RME），在实验验证中，该模型在明确和隐式反馈数据集中均优于现有模型，特别是在用户交互最少的冷启动情况下，RME 在 MovieLens-10M 和 MovieLens-20M 数据集中的表现更佳。

基于用户和物品嵌入的矩阵分解推荐正则化

Regularizing Matrix Factorization with User and Item Embeddings for  Recommendation

We consider the problem of selecting a subset of alternatives given noisy
evaluations of the relative strength of different alternatives. We wish to
select a k-subset (for a given k) that provides a maximum likelihood estimate
for one of several objectives, e.g., containing the strongest alternative.
Although this problem is NP-hard, we show that when the noise level is
sufficiently high, intuitive methods provide the optimal solution. We thus
generalize classical results about singling out one alternative and identifying
the hidden ranking of alternatives by strength. Extensive experiments show that
our methods perform well in practical settings.

本文探讨了在存在噪声评估的情况下，选择替代方案子集的问题，并证明在噪声水平足够高时，直观方法提供了最优解。我们扩展了有关选择单一替代方案以及通过强度识别隐藏排名的经典结果。大量实验表明，我们的方法在实际环境中表现良好。