The text of a review expresses the sentiment a customer has towards a
particular product. This is exploited in sentiment analysis where machine
learning models are used to predict the review score from the text of the
review. Furthermore, the products costumers have purchased in the past are
indicative of the products they will purchase in the future. This is what
recommender systems exploit by learning models from purchase information to
predict the items a customer might be interested in. We propose TransRev, an
approach to the product recommendation problem that integrates ideas from
recommender systems, sentiment analysis, and multi-relational learning into a
joint learning objective. TransRev learns vector representations for users,
items, and reviews. The embedding of a review is learned such that (a) it
performs well as input feature of a regression model for sentiment prediction;
and (b) it always translates the reviewer embedding to the embedding of the
reviewed items. This allows TransRev to approximate a review embedding at test
time as the difference of the embedding of each item and the user embedding.
The approximated review embedding is then used with the regression model to
predict the review score for each item. TransRev outperforms state of the art
recommender systems on a large number of benchmark data sets. Moreover, it is
able to retrieve, for each user and item, the review text from the training set
whose embedding is most similar to the approximated review embedding.

研究提出了 TransRev 方法，将情感分析、推荐系统和多关系学习相结合，学习出用户、物品和评论的向量表示，并通过加权平均来进行评论预测。TransRev 在多个基准数据集上表现优于现有的推荐系统。

TransRev: 将评价建模为用户到商品的翻译

TransRev: Modeling Reviews as Translations from Users to Items

Learning from multiple-relational data which contains noise, ambiguities, or
duplicate entities is essential to a wide range of applications such as
statistical inference based on Web Linked Data, recommender systems,
computational biology, and natural language processing. These tasks usually
require working with very large and complex datasets - e.g., the Web graph -
however, current approaches to multi-relational learning are not practical for
such scenarios due to their high computational complexity and poor scalability
on large data.
In this paper, we propose a novel and scalable approach for multi-relational
factorization based on consensus optimization. Our model, called ConsMRF, is
based on the Alternating Direction Method of Multipliers (ADMM) framework,
which enables us to optimize each target relation using a smaller set of
parameters than the state-of-the-art competitors in this task.
Due to ADMM's nature, ConsMRF can be easily parallelized which makes it
suitable for large multi-relational data. Experiments on large Web datasets -
derived from DBpedia, Wikipedia and YAGO - show the efficiency and performance
improvement of ConsMRF over strong competitors. In addition, ConsMRF
near-linear scalability indicates great potential to tackle Web-scale problem
sizes.

本文提出了一种基于共识优化的新型可扩展的多关系分解方法，名为 ConsMRF，采用 ADMM 框架进行优化，可轻松并行化处理 Web 规模的数据，并在大型 Web 数据集上显示出效率和性能的改进。

使用 ADMM 进行规模化多关系学习

Multi-Relational Learning at Scale with ADMM

Tensor factorizations have become increasingly popular approaches for various
learning tasks on structured data. In this work, we extend the RESCAL tensor
factorization, which has shown state-of-the-art results for multi-relational
learning, to account for the binary nature of adjacency tensors. We study the
improvements that can be gained via this approach on various benchmark datasets
and show that the logistic extension can improve the prediction results
significantly.

本文研究了张量分解在结构化数据方面的应用，扩展了在多关系学习中取得最先进结果的 RESCAL 张量分解，考虑了邻节点张量的二进制特性，并通过采用逻辑扩展方法在多个基准数据集上实现了预测结果的显著改善。