Tomoharu Iwata

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.

Online multiscale dynamic topic models

We propose an online topic model for sequentially analyzing the time evolution of topics in document collections. Topics naturally evolve with multiple timescales. For example, some words may be used consistently over one hundred years, while other words emerge and disappear over periods of a few days. Thus, in the proposed model, current topic-specific distributions over words are assumed to be generated based on the multiscale word distributions of the previous epoch. Considering both the long-timescale dependency as well as the short-timescale dependency yields a more robust model. We derive efficient online inference procedures based on a stochastic EM algorithm, in which the model is sequentially updated using newly obtained data; this means that past data are not required to make the inference. We demonstrate the effectiveness of the proposed method in terms of predictive performance and computational efficiency by examining collections of real documents with timestamps.

Probabilistic latent semantic visualization: topic model for visualizing documents

We propose a visualization method based on a topic model for discrete data such as documents. Unlike conventional visualization methods based on pairwise distances such as multi-dimensional scaling, we consider a mapping from the visualization space into the space of documents as a generative process of documents. In the model, both documents and topics are assumed to have latent coordinates in a two- or three-dimensional Euclidean space, or visualization space. The topic proportions of a document are determined by the distances between the document and the topics in the visualization space, and each word is drawn from one of the topics according to its topic proportions. A visualization, i.e. latent coordinates of documents, can be obtained by fitting the model to a given set of documents using the EM algorithm, resulting in documents with similar topics being embedded close together. We demonstrate the effectiveness of the proposed model by visualizing document and movie data sets, and quantitatively compare it with conventional visualization methods.

Parametric Embedding for Class Visualization

We propose a new method, parametric embedding (PE), that embeds objects with the class structure into a low-dimensional visualization space. PE takes as input a set of class conditional probabilities for given data points and tries to preserve the structure in an embedding space by minimizing a sum of Kullback-Leibler divergences, under the assumption that samples are generated by a gaussian mixture with equal covariances in the embedding space. PE has many potential uses depending on the source of the input data, providing insight into the classifiers behavior in supervised, semisupervised, and unsupervised settings. The PE algorithm has a computational advantage over conventional embedding methods based on pairwise object relations since its complexity scales with the product of the number of objects and the number of classes. We demonstrate PE by visualizing supervised categorization of Web pages, semisupervised categorization of digits, and the relations of words and latent topics found by an unsupervised algorithm, latent Dirichlet allocation.

Fast mining and forecasting of complex time-stamped events

Given huge collections of time-evolving events such as web-click logs, which consist of multiple attributes (e.g., URL, userID, times- tamp), how do we find patterns and trends? How do we go about capturing daily patterns and forecasting future events? We need two properties: (a) effectiveness, that is, the patterns should help us understand the data, discover groups, and enable forecasting, and (b) scalability, that is, the method should be linear with the data size. We introduce TriMine, which performs three-way mining for all three attributes, namely, URLs, users, and time. Specifically TriMine discovers hidden topics, groups of URLs, and groups of users, simultaneously. Thanks to its concise but effective summarization, it makes it possible to accomplish the most challenging and important task, namely, to forecast future events. Extensive experiments on real datasets demonstrate that TriMine discovers meaningful topics and makes long-range forecasts, which are notoriously difficult to achieve. In fact, TriMine consistently outperforms the best state-of-the-art existing methods in terms of accuracy and execution speed (up to 74x faster).

Strength of social influence in trust networks in product review sites

Some popular product review sites such as Epinions allow users to establish a trust network among themselves, indicating who they trust in providing product reviews and ratings. While trust relations have been found to be useful in generating personalised recommendations, the relations between trust and product ratings has so far been overlooked. In this paper, we examine large datasets collected from Epinions and Ciao, two popular product review sites. We discover that in general users who trust each other tend to have smaller differences in their ratings as time passes, giving support to the theories of homophily and social influence. However, we also discover that this does not hold true across all trusted users. A trust relation does not guarantee that two users have similar preferences, implying that personalised recommendations based on trust relations do not necessarily produce more accurate predictions. We propose a method to estimate the strengths of trust relations so as to estimate the true influence among the trusted users. Our method extends the popular matrix factorisation technique for collaborative filtering, which allow us to generate more accurate rating predictions at the same time. We also show that the estimated strengths of trust relations correlate with the similarity among the users. Our work contributes to the understanding of the interplay between trust relations and product ratings, and suggests that trust networks may serve as a more general socialising venue than only an indication of similarity in user preferences.

Fashion coordinates recommender system using photographs from fashion magazines

Fashion magazines contain a number of photographs of fashion models, and their clothing coordinates serve as useful references. In this paper, we propose a recommender system for clothing coordinates using full-body photographs from fashion magazines. The task is that, given a photograph of a fashion item (e.g. tops) as a query, to recommend a photograph of other fashion items (e.g. bottoms) that is appropriate to the query. With the proposed method, we use a probabilistic topic model for learning information about coordinates from visual features in each fashion item region. We demonstrate the effectiveness of the proposed method using real photographs from a fashion magazine and two fashion style sharing services with the task of making top (bottom) recommendations given bottom (top) photographs.

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.

Topic tracking language model for speech recognition

In a real environment, acoustic and language features often vary depending on the speakers, speaking styles and topic changes. To accommodate these changes, speech recognition approaches that include the incremental tracking of changing environments have attracted attention. This paper proposes a topic tracking language model that can adaptively track changes in topics based on current text information and previously estimated topic models in an on-line manner. The proposed model is applied to language model adaptation in speech recognition. We use the MIT OpenCourseWare corpus and Corpus of Spontaneous Japanese in speech recognition experiments, and show the effectiveness of the proposed method.

Topic model for analyzing purchase data with price information

We propose a new topic model for analyzing purchase data with price information. Price is an important factor in consumer purchase behavior. The proposed model assumes that a topic has its own price distributions for each item as well as an item distribution. The topic proportions, which represent a user’s purchase tendency, are influenced by the user’s purchased items and their prices. By estimating the mean and the variance of the price for each topic, the proposed model can cluster related items taking their price ranges into consideration. We present its efficient inference procedure based on collapsed Gibbs sampling. Experiments on real purchase data demonstrate the effectiveness of the proposed model.