%0 Conference Paper %1 balbymarinho2012improving %A Marinho, Leandro Balby %A Nunes, Iury %A Sandholm, Thomas %A Santos, Caio %A Ezequiel, Jordão %A Pires, Carlos Eduardo %B Proceedings of the 18th Brazilian symposium on Multimedia and the Web (WebMedia'12) %C New York, NY, USA %D 2012 %I ACM %K geographic myown recommender temporal %P 293-296 %T Improving Location Recommendations with Temporal Pattern Extraction %U http://doi.acm.org/10.1145/2382636.2382698 %X A key challenge in mobile social media applications is how to present personalised content that is both geographically and temporally relevant. In this paper, we propose a new and generic temporal weighting function for improving location recommendations. First, we identify areas of interest to recommend by clustering geographic activity based on a trace of geotagged photos. Next, the clusters are temporally weighted using TF-IDF, in order to capture seasonality, and a decay scoring function to capture preference drift. Finally, these weights are combined with the cluster scores based on geographic relevance. We evaluate our recommender on a large dataset collected from Panoramio consisting of the top-100 most populated cities in the world and show that incorporating the proposed temporal weighting function improves recommendation quality. %@ 978-1-4503-1706-1

@inproceedings{balbymarinho2012improving, abstract = {A key challenge in mobile social media applications is how to present personalised content that is both geographically and temporally relevant. In this paper, we propose a new and generic temporal weighting function for improving location recommendations. First, we identify areas of interest to recommend by clustering geographic activity based on a trace of geotagged photos. Next, the clusters are temporally weighted using TF-IDF, in order to capture seasonality, and a decay scoring function to capture preference drift. Finally, these weights are combined with the cluster scores based on geographic relevance. We evaluate our recommender on a large dataset collected from Panoramio consisting of the top-100 most populated cities in the world and show that incorporating the proposed temporal weighting function improves recommendation quality.}, added-at = {2012-11-25T21:25:10.000+0100}, address = {New York, NY, USA}, author = {Marinho, Leandro Balby and Nunes, Iury and Sandholm, Thomas and Santos, Caio and Ezequiel, Jordão and Pires, Carlos Eduardo}, biburl = {https://www.bibsonomy.org/bibtex/24f6eb7891ca4e9b6356a249d8ede4b3f/lbalby}, booktitle = {Proceedings of the 18th Brazilian symposium on Multimedia and the Web (WebMedia'12)}, interhash = {3bcdf6667f59ac6dcdfd5c3b72fed221}, intrahash = {4f6eb7891ca4e9b6356a249d8ede4b3f}, isbn = {978-1-4503-1706-1}, keywords = {geographic myown recommender temporal}, pages = {293-296 }, publisher = {ACM}, timestamp = {2014-11-02T20:00:31.000+0100}, title = {Improving Location Recommendations with Temporal Pattern Extraction}, url = {http://doi.acm.org/10.1145/2382636.2382698}, year = 2012 }