Experiments with a Venue-Centric Model for Personalisedand Time-Aware Venue Suggestion

Location-based social networks (LBSNs), such as Foursquare, fostered the emergence of new tasks such as recommending venues a user might wish to visit. In the literature, recommending venues has typically been addressed using user-centric recommendation approaches relying on collaborative filtering techniques. Such approaches not only require many users with detailed profiles to be effective, but they also cannot recommend venues to users who are not actually members of the LBSN. In contrast, in this paper, we introduce a venue-centric yet personalised probabilistic approach that suggests personalised and popular venues for users to visit in the near future. In our approach, we probabilistically incorporate two components, a popularity component for predicting the popularity of a venue at a given point in time, as estimated from the attendance of the venue in the LBSN (i.e. number of check-ins), and a personalisation component for identifying its interestingness with respect to the estimated preferences of the user. The popularity of each venue is predicted using time series forecasting models that are trained on the recent attendance trends of the venue, while the users' interests are modelled from the entity pages that they like on Facebook. Using three major cities, we conduct a user study to evaluate the effectiveness of the two components of our approach in suggesting venues for different types of users at different times of the day. Our experimental results show that an approach that combines the popularity and personalisation components is able to consistently outperform the recommendation service of the leading Foursquare LBSN. We also find that combining popularity and personalisation is effective for both new visitors and residents, while former visitors prefer popular venues.