%0 Conference Paper %1 sandholm2011realtime %A Sandholm, Thomas %A Ung, Hang %B Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation %C New York, NY, USA %D 2011 %I ACM %K cf collaborative filtering geolocation item recommendation recommender spatial %P 14--18 %R 10.1145/1961634.1961638 %T Real-time, location-aware collaborative filtering of web content %U http://doi.acm.org/10.1145/1961634.1961638 %X In this paper we describe the collaborative filtering feature of a location-aware, Web content recommendation service, called <i>Gloe.</i> The main purpose of our collaborative filtering solution is to increase the diversity of recommendations and to thereby mitigate popularity bias. The key challenge is to filter candidate suggestions in real-time, with minimal data mining and model building overhead. There is an apparent trade-off between building general purpose reusable models with contributions from a large user base on one hand and efficient on-line evaluation and recommendation in realtime on the other hand. Our solution is to apply item-based, top-N collaborative filtering within a hierarchical folksonomy structure in a Geohash pre-partitioned geographic locale. We demonstrate that these recommendations can be, on average, as fast to compute as aggregate rating-based recommendations, while offering a more diverse as well as personalized set of recommendations. %@ 978-1-4503-0625-6

@inproceedings{sandholm2011realtime, abstract = {In this paper we describe the collaborative filtering feature of a location-aware, Web content recommendation service, called <i>Gloe.</i> The main purpose of our collaborative filtering solution is to increase the diversity of recommendations and to thereby mitigate popularity bias. The key challenge is to filter candidate suggestions in real-time, with minimal data mining and model building overhead. There is an apparent trade-off between building general purpose reusable models with contributions from a large user base on one hand and efficient on-line evaluation and recommendation in realtime on the other hand. Our solution is to apply item-based, top-N collaborative filtering within a hierarchical folksonomy structure in a Geohash pre-partitioned geographic locale. We demonstrate that these recommendations can be, on average, as fast to compute as aggregate rating-based recommendations, while offering a more diverse as well as personalized set of recommendations.}, acmid = {1961638}, added-at = {2012-09-06T21:28:45.000+0200}, address = {New York, NY, USA}, author = {Sandholm, Thomas and Ung, Hang}, biburl = {https://www.bibsonomy.org/bibtex/268ef269b43fc52d8080989393cfd1fe9/folke}, booktitle = {Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation}, description = {Real-time, location-aware collaborative filtering of web content}, doi = {10.1145/1961634.1961638}, interhash = {399af11c928b418c84ec33c7ea52e178}, intrahash = {68ef269b43fc52d8080989393cfd1fe9}, isbn = {978-1-4503-0625-6}, keywords = {cf collaborative filtering geolocation item recommendation recommender spatial}, location = {Palo Alto, California}, numpages = {5}, pages = {14--18}, publisher = {ACM}, series = {CaRR '11}, timestamp = {2012-09-06T21:28:45.000+0200}, title = {Real-time, location-aware collaborative filtering of web content}, url = {http://doi.acm.org/10.1145/1961634.1961638}, year = 2011 }