Name
Abstract | ||
|---|---|---|
A key challenge in mobile social media applications is how to present personalized 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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2382636.2382698 | WebMedia |
Keywords | Field | DocType |
location recommendation,generic temporal weighting function,temporal pattern extraction,cluster score,key challenge,improving location recommendation,geotagged photo,geographic relevance,proposed temporal weighting function,mobile social media application,geographic activity,large dataset,recommender systems | Recommender system,Data mining,Weighting,Social media,Information retrieval,Location based applications,Computer science,Cluster analysis | Conference |
Citations | PageRank | References |
6 | 0.47 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leandro Balby Marinho | 1 | 702 | 35.57 |
| Iury Nunes | 2 | 8 | 0.88 |
| Thomas Sandholm | 3 | 393 | 30.47 |
| Caio Nóbrega | 4 | 9 | 1.90 |
| Jordão Araújo | 5 | 8 | 0.88 |
| Carlos Eduardo Santos Pires | 6 | 57 | 10.68 |