Location-aware online learning for top-k hashtag recommendation
Róbert Pálovics, Péter Szalai, Levente Kocsis, Júlia Pap, Erzsébet Frigó, András A. Benczúr · Conference on Recommender Systems · 2015
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 ltering 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 dierent top recommendations at dierent times. We describe a time-aware framework based on individual item discounted gain.