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Movie genome: alleviating new item cold start in movie recommendation

机译：电影基因组：缓解电影建议中的新物品冷启动

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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 interactionsa 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.
机译：截至今天，大多数电影推荐服务基于关于使用元数据（例如，流派或演员）的基于协同过滤（CF）和/或基于内容的过滤（CBF）模型的建议。但是，在大多数视频点播和流传输服务中，不断添加新电影和电视系列。 CF模型无法在这种情况下进行预测，因为新添加的视频缺少在技术上被称为新项目冷启动（CS）的Interactiona问题。目前，这个问题最常见的方法是通过利用文本元数据来切换到纯粹的CBF方法。已知这种方法具有比CF更低的精度，因为它忽略了有用的协作信息并依赖于人生成的文本元数据，这是收集和经常容易出错的昂贵。用户生成的内容（例如标记）也可以在CS情况下稀有或不存在。在本文中，我们介绍了一种新的电影推荐系统，通过（i）集成了最先进的音频和视觉描述符来解决电影域中的新项目问题，可以自动从视频内容中提取并构成我们的内容打电话给电影基因组; （ii）利用指定规范相关分析的有效数据融合方法，该方法在我们之前的作品中成功测试了Deldjoo等。 （in：电子商务和网络技术国际会议。Springer，Berlin，PP 34-45,2016B;第十二届ACM会议关于推荐制度的会议。ACM，2018B），更好地利用不同方式之间的互补信息; （iii）提出一种两步混合方法，它在温暖物品（具有交互项目）上培训CF模型，并利用电影基因组上的学习模型建议冷物品（没有交互的物品）。实验验证使用以大规模的现实世界电影推荐数据集进行系统为中心的学习，无论是绝对的冷启动和寒冷的过渡，都在耐寒上;和以用户为中心的在线实验，衡量不同的主观方面，例如满足和多样性。结果表明，与现有方法相比，这种方法的好处。

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