Takeshi Kurashima

Geo topic model: joint modeling of user's activity area and interests for location recommendation

This paper proposes a method that analyzes the location log data of multiple users to recommend locations to be visited. The method uses our new topic model, called Geo Topic Model, that can jointly estimate both the users interests and activity area hosting the users home, office and other personal places. By explicitly modeling geographical features of locations and users, the users interests in other features of locations, which we call latent topics, can be inferred effectively. The topic interests estimated by our model 1) lead to high accuracy in predicting visit behavior as driven by personal interests, 2) make possible the generation of recommendations when the user is in an unfamiliar area (e.g. sightseeing), and 3) enable the recommender system to suggest an interpretable representation of the user profile that can be customized by the user. Experiments are conducted using real location logs of landmark and restaurant visits to evaluate the recommendation performance of the proposed method in terms of the accuracy of predicting visit selections. We also show that our model can estimate latent features of locations such as art, nature and atmosphere as latent topics, and describe each users preference based on them.

Travel route recommendation using geotagged photos

We propose a travel route recommendation method that makes use of the photographers’ histories as held by social photo-sharing sites. Assuming that the collection of each photographer’s geotagged photos is a sequence of visited locations, photo-sharing sites are important sources for gathering the location histories of tourists. By following their location sequences, we can find representative and diverse travel routes that link key landmarks. Recommendations are performed by our photographer behavior model, which estimates the probability of a photographer visiting a landmark. We incorporate user preference and present location information into the probabilistic behavior model by combining topic models and Markov models. Based on the photographer behavior model, proposed route recommendation method outputs a set of personalized travel plans that match the user’s preference, present location, spare time and transportation means. We demonstrate the effectiveness of the proposed method using an actual large-scale geotag dataset held by Flickr in terms of the prediction accuracy of travel behavior.

Probabilistic latent network visualization: inferring and embedding diffusion networks

The diffusion of information, rumors, and diseases are assumed to be probabilistic processes over some network structure. An event starts at one node of the network, and then spreads to the edges of the network. In most cases, the underlying network structure that generates the diffusion process is unobserved, and we only observe the times at which each node is altered/influenced by the process. This paper proposes a probabilistic model for inferring the diffusion network, which we call Probabilistic Latent Network Visualization (PLNV); it is based on cascade data, a record of observed times of node influence. An important characteristic of our approach is to infer the network by embedding it into a low-dimensional visualization space. We assume that each node in the network has latent coordinates in the visualization space, and diffusion is more likely to occur between nodes that are placed close together. Our model uses maximum a posteriori estimation to learn the latent coordinates of nodes that best explain the observed cascade data. The latent coordinates of nodes in the visualization space can 1) enable the system to suggest network layouts most suitable for browsing, and 2) lead to high accuracy in inferring the underlying network when analyzing the diffusion process of new or rare information, rumors, and disease.

Probabilistic identification of visited point-of-interest for personalized automatic check-in

Automatic check-in, which is to identify a users visited points of interest (POIs) from his or her trajectories, is still an open problem because of positioning errors and the high POI density in small areas. In this study, we propose a probabilistic visited-POI identification method. The method uses a new hierarchical Bayesian model for identifying the latent visited-POI label of stay points, which are automatically extracted from trajectories. This model learns from labeled and unlabeled stay point data (i.e., semi-supervised learning) and takes into account personal preferences, stay locations including positioning errors, stay times for each category, and prior knowledge about typical user preferences and stay times. Experimental results with real user trajectories and POIs of Foursquare demonstrated that our method achieved statistically significant improvements in precision at 1 and recall at 3 over the nearest neighbor method and a conventional method that uses a supervised learning-to-rank algorithm.

Inferring Latent Triggers of Purchases with Consideration of Social Effects and Media Advertisements

This paper proposes a method for inferring from single-source data the factors that trigger purchases. Here, single-source data are the histories of item purchases and media advertisement views for each individual. We assume a sequence of purchase events to be a stochastic process incorporating the following three factors: (a) user preference, (b) social effects received from other users, and (c) media advertising effects. As our user-purchase model incorporates the latent relationships between users and advertisers, it can infer the latent triggers of purchases. Experiments on real single-source data show that our model can (a) achieve high prediction accuracy for purchases, (b) discover the key information, i.e., popular items, influential users, and influential advertisers, (c) estimate the relative impact of the three factors on purchases, and (d) find user segments according to the estimated factors.

A Fast and Accurate Method for Estimating People Flow from Spatiotemporal Population Data

Real-time spatiotemporal population data is attracting a great deal of attention for understanding crowd movements in cities. The data is the aggregation of personal location information and consists of just areas and the number of people in each area at certain time instants. Accordingly, it does not explicitly represent crowd movement. This paper proposes a probabilistic model based on collective graphical models that can estimate crowd movement from spatiotemporal population data. There are two technical challenges: (i) poor estimation accuracy as the traditional approach means the model would have too many degrees of freedom, (ii) excessive computation cost. Our key idea for overcoming these two difficulties is to model the transition probability between grid cells (cells hereafter) in a geospatial grid space by using three factors: departure probability of cells, gathering score of cells, and geographical distance between cells. These advances enable us to reduce the degrees of freedom of the model appropriately and derive an efficient estimation algorithm. To evaluate the performance of our method, we conduct experiments using real-world spatiotemporal population data. The results confirm the effectiveness of our method, both in estimation accuracy and computation cost.

Estimating Latent People Flow without Tracking Individuals

Analyzing people flows is important for better navigation and location-based advertising. Since the location information of people is often aggregated for protecting privacy, it is not straightforward to estimate transition populations between locations from aggregated data. Here, aggregated data are incoming and outgoing people counts at each location; they do not contain tracking information of individuals. This paper proposes a probabilistic model for estimating unobserved transition populations between locations from only aggregated data. With the proposed model, temporal dynamics of people flows are assumed to be probabilistic diffusion processes over a network, where nodes are locations and edges are paths between locations. By maximizing the likelihood with flow conservation constraints that incorporate travel duration distributions between locations, our model can robustly estimate transition populations between locations. The statistically significant improvement of our model is demonstrated using real-world datasets of pedestrian data in exhibition halls, bike trip data and taxi trip data in New York City.