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首页> 外文期刊>International journal of multimedia data engineering & management >VideoTopic: Modeling User Interests for Content-Based Video Recommendation
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VideoTopic: Modeling User Interests for Content-Based Video Recommendation

机译：VideoTopic：为基于内容的视频推荐建模用户兴趣

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摘要

With the vast amount of video data uploaded to the Internet every day, how to analyze user interests and recommend videos that they are potentially interested in is a big challenge. Most video recommender systems limit the content to metadata associated with videos, which could lead to poor recommendation results since the metadata is not always available or correct. On the other side, visual content of videos contain information of different granularities, from a whole video, to portions of a video, and to an object in a video, which are not fully explored. This extra information is especially important for recommending new items when no user profile is available. In this paper, a novel recommendation framework, called VideoTopic, that targets at cold-start items is proposed. VideoTopic focuses on user interest modeling and decomposes the recommendation process into interest representation, interest discovery, and recommendation generation. It aims to model user interests by using a topic model to represent the interests in the videos and then discover user interests from user watch histories. A personalized list is generated to maximize the recommendation accuracy by finding the videos that most fit the user's interests under the constraints of some criteria. The optimal solution and a practical system of VideoTopic are presented. Experiments on a public benchmark data set demonstrate the promising results of VideoTopic.
机译：每天都有大量视频数据上传到Internet，如何分析用户兴趣并推荐他们可能感兴趣的视频是一个巨大的挑战。大多数视频推荐器系统会将内容限制为与视频关联的元数据，这可能会导致推荐结果不佳，因为元数据并不总是可用或正确的。另一方面，视频的视觉内容包含不同粒度的信息，从整个视频到视频的各个部分，再到视频中的对象，这些信息没有得到充分研究。当没有可用的用户配置文件时，此额外信息对于推荐新项目尤为重要。在本文中，提出了一个针对冷启动项的新颖推荐框架VideoTopic。 VideoTopic专注于用户兴趣建模，并将推荐过程分解为兴趣表示，兴趣发现和推荐生成。它旨在通过使用主题模型来表示视频中的兴趣来建模用户兴趣，然后从用户观看历史中发现用户兴趣。通过在某些条件的约束下找到最适合用户兴趣的视频，生成个性化列表以最大程度地提高推荐准确性。介绍了VideoTopic的最佳解决方案和实用系统。在公共基准数据集上进行的实验证明了VideoTopic的前景可观。

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