Makoto Nakatsuji

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Graphs are fundamental data structures and have been em-<lb>ployed for centuries to model real-world systems and phe-<lb>nomena. Random walk with restart (RWR) provides a good<lb>proximity score between two nodes in a graph, and it has<lb>been successfully used in many applications such as auto-<lb>matic image captioning, recommender systems, and link(More)
Graphs are a fundamental data structure and have been employed to model objects as well as their relationships. The similarity of objects on the web (e.g., webpages, photos, music, micro-blogs, and social networking service users) is the key to identifying relevant objects in many recent applications. SimRank, proposed by Jeh and Widom, provides a good(More)
Personalize PageRank (PPR) is an effective relevance (proximity) measure in graph mining. The goal of this paper is to efficiently compute single node relevance and top-k/highly relevant nodes without iteratively computing the relevances of all nodes. Based on a "random surfer model", PPR iteratively computes the relevances of all nodes in a graph until(More)
<i>Personalized PageRank (PPR)</i> has been successfully applied to various applications. In real applications, it is important to set PPR parameters in an ad-hoc manner when finding similar nodes because of dynamically changing nature of graphs. Through interactive actions, interactive similarity search supports users to enhance the efficacy of(More)
Most recommender algorithms produce types similar to those the active user has accessed before. This is because they measure user similarity only from the co-rating behaviors against items and compute recommendations by analyzing the items possessed by the users most similar to the active user. In this paper, we define item novelty as the smallest distance(More)
Tracking user interests over time is important for making accurate recommendations. However, the widely-used time-decay-based approach worsens the sparsity problem because it deemphasizes old item transactions. We introduce two ideas to solve the sparsity problem. First, we divide the users’ transactions into epochs i.e. time periods, and identify epochs(More)
Recently, the use of blogs has been a remarkable means to publish user interests. In order to find suitable information resources from a large amount of blog entries which are published every day, we need an information filtering technique to automatically transcribe user interests to a user profile in detail. In this paper, we first classify user blog(More)