Relevance of Non-Activity Representation in Traveling User Behavior Profiling for Adaptive Gamification

@inproceedings{10.1145/3471391.3471431,
  doi = {10.1145/3471391.3471431},
  url = {https://doi.org/10.1145/3471391.3471431},
  isbn = {9781450375979},
  note = {},
  year = {2021},
  month = {},
  pages = {},
  title = {Relevance of Non-Activity Representation in Traveling User Behavior Profiling for Adaptive Gamification},
  author = {María Dalponte Ayastuy and Diego Torres},
  editor = {},
  series = {Interacción '21},
  address = {New York, NY, USA},
  abstract = {Collaborative location collecting systems (CLCS) are collaborative systems where users
collects location-based data. When these systems are gamified and aim to adapt the
game elements to each user, it may require a user traveling behavior profile. This
work presents two approaches of traveling user behavior profiling: a raw series built
up with categorical data that describes the user's activity in a period, and a timed
series that is an enhanced version of the first that includes a representation of
the non-activity time frames. The profiling of user traveling behavior can be used
in adaptive gamification strategies. The approach is evaluated over a behavioral atoms
dataset based on a year of Foursquare check-ins. The results showed that both approaches
reflex different aspects of traveling user behavior, and also both could be used in
a complementary manner.},
  keywords = {Collaborative location collecting systems, Dynamic time warping clustering, User profiling, Adaptive gamification},
  location = {Málaga, Spain},
  numpages = {7},
  articleno = {9},
  booktitle = {Proceedings of the XXI International Conference on Human Computer Interaction},
  publisher = {Association for Computing Machinery},
  organization = {},
}