Noriaki Kawamae

Trend analysis model: trend consists of temporal words, topics, and timestamps

This paper presents a topic model that identifies interpretable low dimensional components in time-stamped data for capturing the evolution of trends. Unlike other models for time-stamped data, our proposal, the trend analysis model (TAM), focuses on the difference between temporal words and other words in each document to detect topic evolution over time. TAM introduces a latent trend class variable into each document and a latent switch variable into each token for handling these differences. The trend class has a probability distribution over temporal words, topics, and a continuous distribution over time, where each topic is responsible for generating words. The latter class uses a document specific probabilistic distribution to judge which variable each word comes from for generating words in each token. Accordingly, TAM can explain which topic co-occurrence pattern will appear at any given time, and represents documents of similar content and timestamp as sharing the same trend class. Therefore, TAM projects them on a latent space of trend dimensionality and allows us to predict the temporal evolution of words and topics in document collections. Experiments on various data sets show that the proposed model can capture interpretable low dimensionality sets of topics and timestamps, take advantage of previous models, and is useful as a generative model in the analysis of the evolution of trends.

Author interest topic model

This paper presents a hierarchical topic model that simultaneously captures topics and authors interests. Our proposal, the Author Interest Topic model (AIT), introduces a latent variable with a separate probability distribution over topics into each document. Experiments on a research paper corpus show that the AIT is useful as a generative model.

Personalized recommendation based on the personal innovator degree

This paper proposes a novel Collaborative Filtering scheme; it focuses on the dynamics and precedence of user preference to recommend items that match the latest preference of the target user. In predicting which items this user will purchase in the near future, the proposed algorithm identifies purchase history logs of users who have similar preferences and a high degree of purchase precedence (i.e., purchasing the same items earlier) relative to the target user. We call this metric the Personal Innovator Degree (PID). Experiments using real online sales data show that the proposed method outperforms existing methods.

Latent interest-topic model: finding the causal relationships behind dyadic data

This paper presents a hierarchical generative model that captures the latent relation of cause and effect underlying user behavioral-originated data such as papers, twitter and purchase history. Our proposel, the Latent Interest Topic model (LIT), introduces a latent variable into each document and each author layor in a coherent generative model. We call the former variable the document class, and the latter variable the author class, where these classes are indicator variables that allow the inclusion of different types of probability, and can be shared over documents with similar content and authors with similar interests, respectively. Significantly, unlike other works, LIT differentiates, respectively, document topics and user interests by using these classes. Consequently, LIT is superior to previous models in explaining the causal relationships behind the data by merging similar distributions; it also makes the computation process easier. Experiments on a research paper corpus show that the proposed model can well capture document and author classes, and reduce the dimensionality of documents to a low-dimensional author-document space, making it useful as a generative model.

Supervised N-gram topic model

We propose a Bayesian nonparametric topic model that rep- resents relationships between given labels and the corre- sponding words/phrases, from supervised articles. Unlike existing supervised topic models, our proposal, supervised N-gram topic model (SNT), focuses on both a number of topics and power-law distribution in the word frequencies to extract topic specific N-grams. To achieve this goal, SNT takes a Bayesian nonparametric approach to topic sampling, which generates word distribution jointly with the given variable in textual order, and then form each N-gram word as a hierarchy of Pitman-Yor process priors. Experiments on labeled text data show that SNT is useful as a generative model for discovering more phrases that complement human experts and domain specific knowledge than the existing al- ternatives. The results show that SNT can be applied to various tasks such as automatic annotation.

Building a conversational model from two-tweets

The current problem in building a conversational model from Twitter data is the scarcity of long conversations. According to our statistics, more than 90% of conversations in Twitter are composed of just two tweets. Previous work has utilized only conversations lasting longer than three tweets for dialogue modeling so that more than a single interaction can be successfully modeled. This paper verifies, by experiment, that two-tweet exchanges alone can lead to conversational models that are comparable to those made from longer-tweet conversations. This finding leverages the value of Twitter as a dialogue corpus and opens the possibility of better conversational modeling using Twitter data.

Trend detection model

This paper presents a topic model that detects topic distributions over time. Our proposed model, Trend Detection Model (TDM) introduces a latent trend class variable into each document. The trend class has a probability distribution over topics and a continuous distribution over time. Experiments using our data set show that TDM is useful as a generative model in the analysis of the evolution of trends.

Hierarchical Approach to Sentiment Analysis

Sentiment analysis aims to extract the customersattitude and feeling from his/her unstructured reviews by separatingthe subjective information from the other information.We propose a generative probabilistic topic model that detectsboth an aspect and corresponding sentiment, simultaneously,from review articles. Unlike existing sentiment analysis models,which generally consider rating prediction to be a side task, ourproposal, the hierarchical approach to sentiment analysis, identifiesboth an item and its rating by dividing topics, traditionallytreated as one entity, into aspect and sentiment topics. Since ourmodel is aware of both objective and subjective information, itcan discover fine-grained tightly coherent topics, and describethe generative process of each article in a unified manner. Tohandle the differences involved, HASA extends topic models byintroducing both observed variables and a latent switch variableinto each token, where topics are influenced not only by word cooccurrencebut also item/rating information, and then classifyingthem as either aspect or sentiment topics. Experiments on reviewarticles show that the proposed model is useful as a generativets from sentiments.

Real Time Recommendations from Connoisseurs

The information overload problem remains serious for both consumers and service/content providers, leading to heightened demands for personalized recommendations. For recommender systems, updating user models is one of the most important tasks to keep up with their changing preferences and trends. Especially since new consumers and items emerge every day, which are promptly rated or reviewed, updating lists of items and rankings is crucial. In this paper, we set the goal of real time recommendation, to present these items instantly. Unlike standard collaborative filtering algorithms, our offline approach focuses only innovative consumers for these predictions, and then uses as few consumers as possible while keeping the same precision. Since innovators exist in many communities, and their opinions will spread and then stimulate their followers to adopt the same behavior, our approach is based on the hypothesis that a set of innova- tive consumers is sufficient to represent the most representative opinions in each community. Following this hypothesis, we derive a scalable method to detect both communities and innovative consumers in each community from a web- scale data from a behavior log. Our evaluation shows that our proposed weighting method can accurately sample given logs, and be compatible only with previous algorithms for real time recommendations.

Theme chronicle model: chronicle consists of timestamp and topical words over each theme

This paper presents a topic model that discovers the correlation patterns in a given time-stamped document collection and how these patterns evolve over time. Our proposal, the theme chronicle model (TCM) divides traditional topics into temporal and stable topics to detect the change of each theme over time; previous topic models ignore these differences and characterize trends as merely bursts of topics. TCM introduces a theme topic (stable topic), a trend topic (temporal topic), timestamps, and a latent switch variable in each token to realize these differences. Its topic layers allow TCM to capture not only word co-occurrence patterns in each theme, but also word co-occurrence patterns at any given time in each theme as trends. Experiments on various data sets show that the proposed model is useful as a generative model to discover fine-grained tightly coherent topics, takes advantage of previous models, and then assigns values for new documents.