Location-aware online learning for top-k recommendation

    We address the problem of recommending highly volatile items for users, both with potentially ambiguous location that may change in time. The three main ingredients of our method include (1) using online machine learning for the highly volatile items; (2) learning the personalized importance of hierarchical geolocation (for example, town, region, country, continent); finally (3) modeling temporal relevance by counting recent items with an exponential decay in recency. For (1), we consider 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. For (2), we observe that trends and geolocation turns out to be more importantthanpersonalizeduserpreferences: user-itemmatrixfactorizationimproves in combination with our geo-trend learning methods, but in itself, it is greatly inferior to our location based models. In fact, since our best performing methods are based on spatiotemporal data, they are applicable in the user cold start setting as well. Finally for (3), we estimate the probability that the item will be viewed by its previous views to obtain a powerful model that combines item popularity and recency. To generate realistic data for measuring our new methods, we rely on Twitter users with known GPS location and consider hashtags as items that we recommend the users to be included in their next message.

    R Pálovics, P Szalai, J Pap, E Frigó, L Kocsis, AA Benczúr
    Pervasive and Mobile Computing