Film media is a rich form of artistic expression. Unlike photography, and
short videos, movies contain a storyline that is deliberately complex and
intricate in order to engage its audience. In this paper we present a large
scale study comparing the effectiveness of visual, audio, text, and
metadata-based features for predicting high-level information about movies such
as their genre or estimated budget. We demonstrate the usefulness of
content-based methods in this domain in contrast to human-based and
metadata-based predictions in the era of deep learning. Additionally, we
provide a comprehensive study of temporal feature aggregation methods for
representing video and text and find that simple pooling operations are
effective in this domain. We also show to what extent different modalities are
complementary to each other. To this end, we also introduce Moviescope, a new
large-scale dataset of 5,000 movies with corresponding movie trailers (video +
audio), movie posters (images), movie plots (text), and metadata.

本文旨在比较不同类型的视觉、音频、文本和基于元数据的特征对于预测电影的高层信息（如类型或预计预算）的有效性，并介绍了一个新的 Moviescope 数据集，该数据集包括 5000 部电影以及对应的预告片、海报、剧情和元数据。作者实证了在深度学习时代，基于内容的方法相对于基于人类和基于元数据的预测方法在该领域的有效性，并探究了表示视频和文本的时间特征聚合方法的有效性。此外，研究团队还展示了不同模态之间互补性的程度，并发现简单池化操作在该领域是有效的。

Moviescope: 利用多种模态分析电影的大规模研究

Moviescope: Large-scale Analysis of Movies using Multiple Modalities

In this paper, we propose a probabilistic generative model, called unified
model, which naturally unifies the ideas of social influence, collaborative
filtering and content-based methods for item recommendation. To address the
issue of hidden social influence, we devise new algorithms to learn the model
parameters of our proposal based on expectation maximization (EM). In addition
to a single-machine version of our EM algorithm, we further devise a
parallelized implementation on the Map-Reduce framework to process two
large-scale datasets we collect. Moreover, we show that the social influence
obtained from our generative models can be used for group recommendation.
Finally, we conduct comprehensive experiments using the datasets crawled from
last.fm and whrrl.com to validate our ideas. Experimental results show that the
generative models with social influence significantly outperform those without
incorporating social influence. The unified generative model proposed in this
paper obtains the best performance. Moreover, our study on social influence
finds that users in whrrl.com are more likely to get influenced by friends than
those in last.fm. The experimental results also confirm that our social
influence based group recommendation algorithm outperforms the state-of-the-art
algorithms for group recommendation.

本研究提出了一个概率生成模型 —— 统一模型，该模型自然地将社会影响、协同过滤和基于内容的方法统一起来，用于物品推荐。我们通过期望最大化算法（EM）来设定模型参数，实验结果显示带有社会影响的生成模型显著优于不带社会影响的模型，并且统一模型表现最佳。此外，我们发现 Whrrl.com 上的用户更可能受到朋友的影响，并且我们基于社会影响的群组推荐算法也优于最先进的算法。

基于概率生成模型的社交影响推荐探索

Exploring Social Influence for Recommendation - A Probabilistic  Generative Model Approach

Recommender systems improve access to relevant products and information by
making personalized suggestions based on previous examples of a user's likes
and dislikes. Most existing recommender systems use social filtering methods
that base recommendations on other users' preferences. By contrast,
content-based methods use information about an item itself to make suggestions.
This approach has the advantage of being able to recommended previously unrated
items to users with unique interests and to provide explanations for its
recommendations. We describe a content-based book recommending system that
utilizes information extraction and a machine-learning algorithm for text
categorization. Initial experimental results demonstrate that this approach can
produce accurate recommendations.

该研究描述了一种利用信息提取和机器学习算法进行文本分类的基于内容的书籍推荐系统，初步实验结果表明该方法可以产生准确的推荐。