The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Generating diverse and representative image search results for landmarks
Proceedings of the 17th international conference on World Wide Web
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A Spatial User Similarity Measure for Geographic Recommender Systems
GeoS '09 Proceedings of the 3rd International Conference on GeoSpatial Semantics
Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
Factorization models for context-/time-aware movie recommendations
Proceedings of the Workshop on Context-Aware Movie Recommendation
Exploiting geographical influence for collaborative point-of-interest recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Identifying points of interest by self-tuning clustering
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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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.