Noriko Takaya

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.

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.

Tracking Temporal Dynamics of Purchase Decisions via Hierarchical Time-Rescaling Model

Improvements in information technology have made it easier for industry to communicate with their customers, raising hopes for a scheme that can estimate when customers will want to make purchases. Although a number of models have been developed to estimate the time-varying purchase probability, they are based on very restrictive assumptions such as preceding purchase-event dependence and discrete-time effect of covariates. Our preliminary analysis of real-world data finds that these assumptions are invalid: self-exciting behavior, as well as marketing stimulus and preceding purchase dependence, should be examined as possible factors influencing purchase probability. In this paper, by employing the novel idea of hierarchical time rescaling, we propose a tractable but highly flexible model that can meld various types of intrinsic history dependency and marketing stimuli in a continuous-time setting. By employing the proposed model, which incorporates the three factors, we analyze actual data, and show that our model has the ability to precisely track the temporal dynamics of purchase probability at the level of individuals. It enables us to take effective marketing actions such as advertising and recommendations on timely and individual bases, leading to the construction of a profitable relationship with each customer.

Nonparametric hierarchal bayesian modeling in non-contractual heterogeneous survival data

An important problem in the non-contractual marketing domain is discovering the customer lifetime and assessing the impact of customers characteristic variables on the lifetime. Unfortunately, the conventional hierarchical Bayes model cannot discern the impact of customers characteristic variables for each customer. To overcome this problem, we present a new survival model using a non-parametric Bayes paradigm with MCMC. The assumption of a conventional model, logarithm of purchase rate and dropout rate with linear regression, is extended to include our assumption of the Dirichlet Process Mixture of regression. The extension assumes that each customer belongs probabilistically to different mixtures of regression, thereby permitting us to estimate a different impact of customer characteristic variables for each customer. Our model creates several customer groups to mirror the structure of the target data set. The effectiveness of our proposal is confirmed by a comparison involving a real e-commerce transaction dataset and an artificial dataset; it generally achieves higher predictive performance. In addition, we show that preselecting the actual number of customer groups does not always lead to higher predictive performance.