Previous works related to automatic personality recognition focus on using
traditional classification models with linguistic features. However, attentive
neural networks with contextual embeddings, which have achieved huge success in
text classification, are rarely explored for this task. In this project, we
have two major contributions. First, we create the first dialogue-based
personality dataset, FriendsPersona, by annotating 5 personality traits of
speakers from Friends TV Show through crowdsourcing. Second, we present a novel
approach to automatic personality recognition using pre-trained contextual
embeddings (BERT and RoBERTa) and attentive neural networks. Our models largely
improve the state-of-art results on the monologue Essays dataset by 2.49%, and
establish a solid benchmark on our FriendsPersona. By comparing results in two
datasets, we demonstrate the challenges of modeling personality in multi-party
dialogue.

本文创建了首个基于对话的个性化数据集，并使用预先训练的语境嵌入和注意力神经网络等方法探索自动个性识别新方法，通过对两种数据集的比较，展示了在多方对话中建模个性所面临的挑战。

使用注意力网络和上下文嵌入进行单人独白和多人对话的自动文本个性识别

Automatic Text-based Personality Recognition on Monologues and  Multiparty Dialogues Using Attentive Networks and Contextual Embeddings

Images account for a significant part of user decisions in many application
scenarios, such as product images in e-commerce, or user image posts in social
networks. It is intuitive that user preferences on the visual patterns of image
(e.g., hue, texture, color, etc) can be highly personalized, and this provides
us with highly discriminative features to make personalized recommendations.
Previous work that takes advantage of images for recommendation usually
transforms the images into latent representation vectors, which are adopted by
a recommendation component to assist personalized user/item profiling and
recommendation. However, such vectors are hardly useful in terms of providing
visual explanations to users about why a particular item is recommended, and
thus weakens the explainability of recommendation systems.
As a step towards explainable recommendation models, we propose visually
explainable recommendation based on attentive neural networks to model the user
attention on images, under the supervision of both implicit feedback and
textual reviews. By this, we can not only provide recommendation results to the
users, but also tell the users why an item is recommended by providing
intuitive visual highlights in a personalized manner. Experimental results show
that our models are not only able to improve the recommendation performance,
but also can provide persuasive visual explanations for the users to take the
recommendations.

本文提出了基于注意力神经网络的可视化解释推荐模型，这种模型可同时提供用户感兴趣的推荐结果和推荐理由，其关键在于模拟用户注意力，使用隐式反馈和文本评价进行监督学习。实验结果表明，所提出的模型不仅可提高推荐性能，还可以提供具有说服力的视觉解释。