Context-Aware Recommendations on the Mobile Web

Abstract

Recently, there has been a significant increase in the use of data via the mobile web. Since the user interfaces for mobile devices are inconvenient for browsing through many pages and searching their contents, many studies have focused on ways to recommend content or menus that users prefer. However, the mobile usage pattern of content or services differs according to context. In this paper, we apply context information—location, time, identity, activity, and device—to recommend services or content on the mobile web. A Korean mobile service provider has implemented context-aware recommendations. The usage logs of this service are analyzed to show the performance of context-aware recommendations.