As of today, most movie recommendation services base their recommendations on collaborative filtering (CF) and/or content-based filtering (CBF) models that use metadata (e.g., genre or cast). In most video-on-demand and streaming services, however, new movies and TV series are continuously added. CF models are unable to make predictions in such a scenario, since the newly added videos lack interactions—a problem technically known as new item cold start (CS). Currently, the most common approach to this problem is to switch to a purely CBF method, usually by exploiting textual metadata. This approach is known to have lower accuracy than CF because it ignores useful collaborative information and relies on human-generated textual metadata, which are expensive to collect and often prone to errors. User-generated content, such as tags, can also be rare or absent in CS situations. In this paper, we introduce a new movie recommender system that addresses the new item problem in the movie domain by (i) integrating state-of-the-art audio and visual descriptors, which can be automatically extracted from video content and constitute what we call the movie genome; (ii) exploiting an effective data fusion method named canonical correlation analysis, which was successfully tested in our previous works Deldjoo et al. (in: International Conference on Electronic Commerce and Web Technologies. Springer, Berlin, pp 34–45, 2016b; Proceedings of the Twelfth ACM Conference on Recommender Systems. ACM, 2018b), to better exploit complementary information between different modalities; (iii) proposing a two-step hybrid approach which trains a CF model on warm items (items with interactions) and leverages the learned model on the movie genome to recommend cold items (items without interactions). Experimental validation is carried out using a system-centric study on a large-scale, real-world movie recommendation dataset both in an absolute cold start and in a cold to warm transition; and a user-centric online experiment measuring different subjective aspects, such as satisfaction and diversity. Results show the benefits of this approach compared to existing approaches.

中文翻译：

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电影基因组：缓解电影推荐中的新项目冷启动

截至今天，大多数电影推荐服务基于使用元数据（例如，流派或演员表）的协同过滤 (CF) 和/或基于内容的过滤 (CBF) 模型进行推荐。然而，在大多数视频点播和流媒体服务中，新电影和电视剧不断增加。CF 模型无法在这种情况下进行预测，因为新添加的视频缺乏交互——这个问题在技术上被称为新项目冷启动 (CS)。目前，解决此问题的最常见方法是切换到纯粹的 CBF 方法，通常是利用文本元数据。众所周知，这种方法的准确性低于 CF，因为它忽略了有用的协作信息并依赖于人工生成的文本元数据，而这些元数据的收集成本高昂且往往容易出错。用户生成的内容，例如标签、在 CS 情况下也可能很少见或不存在。在本文中，我们介绍了一种新的电影推荐系统，该系统通过（i）集成最先进的音频和视觉描述符来解决电影领域中的新项目问题，这些描述符可以从视频内容中自动提取并构成我们调用电影基因组；(ii) 利用一种名为规范相关分析的有效数据融合方法，该方法已在我们之前的工作 Deldjoo 等人中成功测试。(in: International Conference on Electronic Commerce and Web Technologies. Springer, Berlin, pp 34–45, 2016b; Proceedings of the Twelfth ACM Conference on Recommender Systems. ACM, 2018b)，以更好地利用不同模式之间的互补信息；(iii) 提出了一种两步混合方法，该方法在暖项目（具有交互的项目）上训练 CF 模型，并利用电影基因组上的学习模型来推荐冷项目（没有交互的项目）。实验验证是使用以系统为中心的研究在绝对冷启动和冷到暖过渡的大规模真实电影推荐数据集上进行的；以及以用户为中心的在线实验，测量不同的主观方面，例如满意度和多样性。结果显示了这种方法与现有方法相比的优势。真实世界电影推荐数据集，无论是在绝对冷启动还是从冷到暖的过渡；以及以用户为中心的在线实验，测量不同的主观方面，例如满意度和多样性。结果显示了这种方法与现有方法相比的优势。真实世界电影推荐数据集，无论是在绝对冷启动还是从冷到暖的过渡；以及以用户为中心的在线实验，测量不同的主观方面，例如满意度和多样性。结果显示了这种方法与现有方法相比的优势。