Thomas Maurer just finished his master’s thesis. The goal of his master’s thesis was to use the location information about people with similar preferences rather than structured information about places to identify interesting locations. The underlying assumption is that locations get interesting when people with common interests meet there to have a great time together. Further, people’s location history data should be used to create a predictive model to forecast when locations get interesting for all the different people. In addition, the daily weather forecast metaphor should be used to visualize the future changes of areas getting interesting.
And of course, create a mobile app!
The concept of recommendation clouds specifies an area where people with common interests get together at the same time and form a cloud-like shape.
Abstract from the master’s thesis
Recommender systems are a type of information retrieval and filtering systems that try to propose items to users according to their individual preferences. Collaborative Filtering is a method to implement such a recommender system through the prediction of ratings for items based on the social environment of the user. In a location recommender system the recommended items are locations, places or areas of interest. Commonly such location recommendations focus only on the current location of the user leaving out other important contextual factors such as time and the locations of other users.
This thesis builds on the assumption that users might be interested in places or areas where other users with similar preferences currently are situated. We developed a visualization following the metaphor of a heatmap – e.g. used of precipitation radar images – where the locations of users are drawn on a map and shape clouds which recommend areas of interest visually. In addition, we develop an abstracted view of the cloud visualization called projection which recommends areas and places depending on hour, weekday and user preferences. We present our implementation of such a location recommender system, in particular the visualizations. Finally, we evaluate our visualization recommendation approach with a synthetic data set against other collaborative filtering algorithms and can present eligible results.
Thomas Maurer: Environs: visualization of recommendation clouds on the iPhone, University of Zurich, Faculty of Economics, 2010. (Master Thesis)
Editors: Harald C. Gall, Amancio Bouza