| dc.description.abstract | With the development of smart devices and the convenient of social media platform, people
are now more open in sharing their photos while traveling. Geo-tagged data from these
photos and user’s additional multisource data such as weather combined, could help us in
modelling user’s preferences profile in details. Therefore, exploiting these geo-tagged data,
a personalized recommendation model is built to provide recommendations based on
personal interested analysis. Retrieving geo data from Gowalla dataset and context data
from weather API, we posed the recommendation model as a two-stages architect problem
where we first generate a candidate lists (matching candidates) and ranking the list follow
user’s modelled preferences afterwards. The dataset was formally clustered into venue
centres, then transformed into a multi-class classification problem in the matching process
using supported vector machine algorithm, and gradient boosting regressor is deployed to
rank the generated list follow preference scoring. Evaluating the model by benchmarking
with the one-stage architect by accuracy metrics and ranking metric, the model shows its
improvement in handling mitigating cold-start problem and mining long-tail data. | en_US |
