We tackle the blog recommendation problem in Tumblr for mobile users in this paper. Blog recommendation is challenging since most mobile users would suffer from the cold start when there are only a limited number of blogs followed by the user. Specifically to address this problem in the mobile domain, we take into account mobile apps, which typically provide rich information from the users. Based on the assumption that the user interests can be reflected from their app usage patterns, we propose to exploit the app usage data for improving blog recommendation. Building on the state-of-the-art recommendation framework, Factorization Machines (FM), we implement app-based FM that integrates app usage data with the user-blog follow relations. In this approach the blog recommendation is generated not only based on the blogs that the user followed before, but also the apps that the user has often used. We demonstrate in a series of experiments that app-based FM can outperform other alternative approaches to a significant extent. Our experimental results also show that exploiting app usage information is particularly effective for improving blog recommendation quality for cold start users.
翻译:我们处理Tumblr的博客建议问题。 博客建议具有挑战性, 因为大多数移动用户会因为用户所遵循的博客数量有限而陷入寒冷的开端。 具体地说, 为了解决移动域中的这个问题, 我们考虑到移动应用程序, 通常能提供来自用户的丰富信息。 基于用户的兴趣可以从应用程序使用模式中得到反映的假设, 我们提议利用应用程序使用数据来改进博客建议。 我们的实验结果还表明, 利用应用程序使用信息对于提高冷启动用户的博客建议质量特别有效。