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PBR: A Personalized Book Resource Recommendation System

机译:PBR:个性化的书资源推荐系统

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摘要

Recommendation system is widely applied for online resource retrieval, especially in digital publishing industry. A good recommendation system can help the users to efficiently find the desirable reading materials from the massive online resources. However, the conventional recommendation systems are always facing the cold-start problem, and it is difficult to provide the personalized service in an efficient way, since the users' preference may change sometimes. To address the problems above, this work introduces a personalized book resource recommendation system, which well utilizes the tag information of book resources to interact with the users. The user feedback will deliver their real-time preference, and the system can provide more precise recommendation candidates to improve the service quality. In this demo, we will introduce the overall framework and some important modules of the recommendation system, with relevant technical details. We will show the system functions by providing the visual results of the actual book resource recommendation.
机译:推荐系统广泛应用于在线资源检索,尤其是数字出版业。一个好的推荐系统可以帮助用户有效地从大规模的在线资源中找到所需的阅读材料。然而,传统推荐系统总是面临冷启动问题,并且很难以有效的方式提供个性化服务,因为用户的偏好有时可能改变。为了解决上述问题,这项工作介绍了个性化的图书资源推荐系统,利用书资源的标签信息与用户进行交互。用户反馈将提供其实时偏好,系统可以提供更精确的推荐候选人来提高服务质量。在这个演示中,我们将介绍一下建议制度的整体框架和一些重要的模块,具有相关的技术细节。我们将通过提供实际的图书资源推荐的视觉结果来显示系统功能。

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