Location-Aware Online Learning for Top-k Hashtag Recommendation

In this paper we investigate the problem of recommending Twitter hashtags for users with known GPS location, learning online from the stream of geo-tagged tweets. Our method learns the relevance of regions in a geographical hierarchy, combined with the local popularity of the hashtag. Unlike in typical collaborative filtering settings, trends and geolocation turns out to be more important than personalized user preferences. We evaluate in a time-aware setting, where evaluation is cumbersome by traditional measures, since we have different top recommendations at different times. We describe a time-aware framework based on individual item discounted gain.