Mobile news recommender systems help users retrieve news that is relevant in their particular context and can be presented in ways that require minimal user interaction. In spite of the availability of contextual information about mobile users, though, current mobile news applications employ rather simple strategies for news recommendation. Our multi-perspective approach unifies temporal, locational, and preferential information to provide a more fine-grained recommendation strategy. This demo paper presents the implementation of our solution to efficiently recommend specific news articles from a large corpus of newly-published press releases in a way that closely matches a reader's reading preferences.
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