Over the past years, the research of video recommender systems (RSs) has been mainly focussed on the development of novel algorithms. Although beneficial, still any algorithm may fail to recommend video items that the system has no form of data associated to them (New Item Cold Start). This problem occurs when a new item is added to the catalogue of the system and no data are available for that item. In content‐based RSs, the video items are typically represented by semantic attributes, when generating recommendations. These attributes require a group of experts or users for annotation, and still, the generated recommendations might not capture a complete picture of the users' preferences, for example, the visual tastes of users on video style. This article addresses this problem by proposing recommendation based on novel visual features that do not require human annotation and can represent visual aspects of video items. We have designed a novel evaluation methodology considering three realistic scenarios, that is, (a) extreme cold start, (b) moderate cold start and (c) warm‐start scenario. We have conducted a set of comprehensive experiments, and our results have shown the superior performance of recommendations based on visual features, in all of the evaluation scenarios.

中文翻译：

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通过将视觉功能与受限的Boltzmann机器相结合来解决视频推荐系统中的新项目问题

在过去的几年中，视频推荐系统（RS）的研究主要集中在新颖算法的开发上。尽管有好处，但是任何算法都可能无法推荐系统没有与之关联的任何形式的数据的视频项目（“新项目冷启动”）。当一个新的项目添加到系统的目录，并且没有数据可用于该项目时，会发生此问题。在基于内容的RS中，当生成推荐时，视频项通常由语义属性表示。这些属性需要一组专家或用户进行注释，但是，生成的推荐可能无法捕获用户偏好的完整图片，例如，用户对视频风格的视觉品味。本文通过基于新颖的视觉特征提出建议来解决此问题，这些视觉特征不需要人工注释，并且可以表示视频项目的视觉方面。我们设计了一种新颖的评估方法，其中考虑了三种现实情况，即（a）极端冷启动，（b）中度冷启动和（c）热启动场景。我们进行了一系列综合实验，我们的结果表明，在所有评估方案中，基于视觉特征的建议均具有出色的性能。