The Apps You Use Bring The Blogs to Follow

TL;DR

This paper introduces an app-based factorization machine approach to enhance blog recommendations on Tumblr for mobile users, especially addressing cold start issues by leveraging app usage data.

Contribution

It proposes integrating app usage data into recommendation models, significantly improving blog suggestions for new users in mobile environments.

Findings

App-based FM outperforms other methods in accuracy.

Exploiting app data benefits cold start user recommendations.

Significant improvement in recommendation quality demonstrated.

Abstract

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.