The relationship between electricity demand and weather has been established
for a long time and is one of the cornerstones in load prediction for operation
and planning, along with behavioral and social aspects such as calendars or
significant events. This paper explores how and why the social information
contained in the news can be used better to understand aggregate population
behaviour in terms of energy demand. The work is done through experiments
analysing the impact of predicting features extracted from national news on
day-ahead electric demand prediction. The results are compared to a benchmark
model trained exclusively on the calendar and meteorological information.
Experimental results showed that the best-performing model reduced the official
standard errors around 4%, 11%, and 10% in terms of RMSE, MAE, and SMAPE. The
best-performing methods are: word frequency identified COVID-19-related
keywords; topic distribution that identified news on the pandemic and internal
politics; global word embeddings that identified news about international
conflicts. This study brings a new perspective to traditional electricity
demand analysis and confirms the feasibility of improving its predictions with
unstructured information contained in texts, with potential consequences in
sociology and economics.

本文探讨了如何更好地利用新闻中包含的社会信息来理解能源需求方面的人口总体行为，并通过实验分析从国家新闻中提取的预测特征对日前电力需求预测的影响，实验结果表明最佳表现模型可将官方标准误差在 RMSE、MAE 和 SMAPE 方面降低约 4%、11% 和 10%，最佳表现方法包括确定 COVID-19 相关关键字的词频、识别流行病和内部政治新闻的主题分布，识别国际冲突新闻的全球词嵌入，为传统电力需求分析带来了新的视角并确认利用文本中的非结构化信息改进其预测的可行性，具有潜在的社会学和经济学影响。

自然语言处理在电力需求分析中的定量探讨

A Quantitative Exploration of Natural Language Processing Applications for Electricity Demand Analysis

Nowadays, privacy preserving machine learning has been drawing much attention
in both industry and academy. Meanwhile, recommender systems have been
extensively adopted by many commercial platforms (e.g. Amazon) and they are
mainly built based on user-item interactions. Besides, social platforms (e.g.
Facebook) have rich resources of user social information. It is well known that
social information, which is rich on social platforms such as Facebook, are
useful to recommender systems. It is anticipated to combine the social
information with the user-item ratings to improve the overall recommendation
performance. Most existing recommendation models are built based on the
assumptions that the social information are available. However, different
platforms are usually reluctant to (or cannot) share their data due to certain
concerns. In this paper, we first propose a SEcure SOcial RECommendation
(SeSoRec) framework which can (1) collaboratively mine knowledge from social
platform to improve the recommendation performance of the rating platform, and
(2) securely keep the raw data of both platforms. We then propose a Secret
Sharing based Matrix Multiplication (SSMM) protocol to optimize SeSoRec and
prove its correctness and security theoretically. By applying minibatch
gradient descent, SeSoRec has linear time complexities in terms of both
computation and communication. The comprehensive experimental results on three
real-world datasets demonstrate the effectiveness of our proposed SeSoRec and
SSMM.

本研究提出 SeSoRec 框架，并基于 Secret Sharing based Matrix Multiplication 协议优化其算法。实证结果表明，SeSoRec 对于保护用户数据和提高推荐性能是有效的。

基于秘密分享的安全社交推荐

Secure Social Recommendation based on Secret Sharing

This paper is concerned with how to make efficient use of social information
to improve recommendations. Most existing social recommender systems assume
people share similar preferences with their social friends. Which, however, may
not hold true due to various motivations of making online friends and dynamics
of online social networks. Inspired by recent causal process based
recommendations that first model user exposures towards items and then use
these exposures to guide rating prediction, we utilize social information to
capture user exposures rather than user preferences. We assume that people get
information of products from their online friends and they do not have to share
similar preferences, which is less restrictive and seems closer to reality.
Under this new assumption, in this paper, we present a novel recommendation
approach (named SERec) to integrate social exposure into collaborative
filtering. We propose two methods to implement SERec, namely social
regularization and social boosting, each with different ways to construct
social exposures. Experiments on four real-world datasets demonstrate that our
methods outperform the state-of-the-art methods on top-N recommendations.
Further study compares the robustness and scalability of the two proposed
methods.

本研究旨在探讨如何有效地利用社交信息来改进推荐，提出了一种将社交曝光融入协同过滤的新颖方法，通过社交规范化和社交激励两种不同的方式构建社交曝光，实验证明此方法优于当前的最新方法，并进行了两种方法的可靠性和可扩展性比较。