With the increasing popularity of location-based social media applications and devices that automatically tag generated content with locations, large repositories of collaborative geo-referenced data are appearing on-line. Efficiently extracting user preferences from these data to determine what information to recommend is challenging because of the sheer volume of data as well as the frequency of updates. Traditional recommender systems focus on the interplay between users and items, but ignore contextual parameters such as location. In this paper we take a geospatial approach to determine locational preferences and similarities between users. We propose to capture the geographic context of user preferences for items using a relational graph, through which we are able to derive many new and state-of-the-art recommendation algorithms, including combinations of them, requiring changes only in the definition of the edge weights. Furthermore, we discuss several solutions for cold-start scenarios. Finally, we conduct experiments using two real-world datasets and provide empirical evidence that many of the proposed algorithms outperform existing location-aware recommender algorithms.
With the increasing popularity of location-based services, such as tour guide and location-based social network, we now have accumulated many location data on the Web. In this paper, we show that, by using the location data based on GPS and users' comments at various locations, we can discover interesting locations and possible activities that can be performed there for recommendations. Our research is highlighted in the following location-related queries in our daily life: 1) if we want to do something such as sightseeing or food-hunting in a large city such as Beijing, where should we go? 2) If we have already visited some places such as the Bird's Nest building in Beijing's Olympic park, what else can we do there? By using our system, for the first question, we can recommend her to visit a list of interesting locations such as Tiananmen Square, Bird's Nest, etc. For the second question, if the user visits Bird's Nest, we can recommend her to not only do sightseeing but also to experience its outdoor exercise facilities or try some nice food nearby. To achieve this goal, we first model the users' location and activity histories that we take as input. We then mine knowledge, such as the location features and activity-activity correlations from the geographical databases and the Web, to gather additional inputs. Finally, we apply a collective matrix factorization method to mine interesting locations and activities, and use them to recommend to the users where they can visit if they want to perform some specific activities and what they can do if they visit some specific places. We empirically evaluated our system using a large GPS dataset collected by 162 users over a period of 2.5 years in the real-world. We extensively evaluated our system and showed that our system can outperform several state-of-the-art baselines. We implemented the Prediction-by-Partial-Match data compression algorithm as a predictor of future locations. Positioning was done using IEEE 802.11 wireless access logs. Several experiments were run to determine how to divide the data for training and testing and how to best represent the data as a string of symbols. Our test data consisted of 198 datasets containing over 28,000 pairs, obtained from the UCSD Wireless Topology Discovery project. Tests of a first-order PPM model revealed a 90 success rate in predicting a user's location given the time. The third-order model, which is given the previous time and location and asked to predict the location at a given time, is correct 92 of the time. 