Makoto Nakatsuji

Classical music for rock fans?: novel recommendations for expanding user interests

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 from the class the user accessed before to the class that includes target items over the taxonomy. Then, we try to accurately recommend highly novel items to the user. First, our method measures user similarity by employing items rated by users and a taxonomy of items. It can accurately identify many items that may suit the user. Second, it creates a graph whose nodes are users; weighted edges are set between users according to their similarity. It analyzes the user graph and extracts users that are related on the graph though the similarity between the active user and each of those users is not high. The users so extracted are likely to have highly novel items for the active user. An evaluation conducted on several datasets finds that our method accurately identifies items with higher novelty than previous methods.

Efficient search algorithm for SimRank

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 similarity score and has been successfully used in many applications such as web spam detection, collaborative tagging analysis, link prediction, and so on. SimRank computes similarities iteratively, and it needs O(N4T) time and O(N2) space for similarity computation where N and T are the number of nodes and iterations, respectively. Unfortunately, this iterative approach is computationally expensive. The goal of this work is to process top-k search and range search efficiently for a given node. Our solution, SimMat, is based on two ideas: (1) It computes the approximate similarity of a selected node pair efficiently in non-iterative style based on the Sylvester equation, and (2) It prunes unnecessary approximate similarity computations when searching for the high similarity nodes by exploiting estimations based on the Cauchy-Schwarz inequality. These two ideas reduce the time and space complexities of the proposed approach to O(Nn) where n is the target rank of the low-rank approximation (n ≪ N in practice). Our experiments show that our approach is much faster, by several orders of magnitude, than previous approaches in finding the high similarity nodes.

Efficient ad-hoc search for personalized PageRank

Personalized PageRank (PPR) 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 applications. Unfortunately, if the graph is large, interactive similarity search is infeasible due to its high computation cost. Previous PPR approaches cannot effectively handle interactive similarity search since they need precomputation or approximate computation of similarities. The goal of this paper is to efficiently find the top-k nodes with exact node ranking so as to effectively support interactive similarity search based on PPR. Our solution is Castanet. The key Castanet operations are (1) estimate upper/lower bounding similarities iteratively, and (2) prune unnecessary nodes dynamically to obtain top-k nodes in each iteration. Experiments show that our approach is much faster than existing approaches.

Adaptive Message Update for Fast Affinity Propagation

Affinity Propagation is a clustering algorithm used in many applications. It iteratively updates messages between data points until convergence. The message updating process enables Affinity Propagation to have higher clustering quality compared with other approaches. However, its computation cost is high; it is quadratic in the number of data points. This is because it updates the messages of all data point pairs. This paper proposes an efficient algorithm that guarantees the same clustering results as the original algorithm. Our approach, F-AP, is based on two ideas: (1) it computes upper and lower estimates to limit the messages to be updated in each iteration, and (2) it dynamically detects converged messages to efficiently skip unneeded updates. Experiments show that F-AP is much faster than previous approaches with no loss in clustering performance.

Proposal and Verification of Flexible Interface Mapping Technique for Automatic System Cooperation Based on Semantics

These days, many companies are executing their business aims based on decentralized cooperation of software components which work on various systems over a network. However, messages and processes between systems are designed individually in each operations division. Therefore, the system development for adjusting interfaces is expensive, so the companies cannot introduce their services in a dynamic business environment. To resolve such problems, we propose interface modeling technique and message mapping technique which model the relationship between the message formats and semantics on the formats by using Web Ontology Language (OWL) and execute the mapping between the message formats by using semantics. We developed the user interactive message mapping tool and evaluated our proposed methods based on the interface specifications of real network management systems.

Semantic Social Network Analysis by Cross-Domain Tensor Factorization

Analyzing “what topics” a user discusses with others is important in social network analysis. Since social relationships can be represented as multiobject relationships (e.g., those composed of a user, another user, and the topic of communication), they can be naturally represented as a tensor. By factorizing the tensor, we can perform communication prediction that predicts links among users and the topics discussed among them. The prediction accuracy, however, is often inadequate for applications because: 1) users usually discuss a variety of topics, and thus the prediction results tend to be biased toward popular domains and 2) topics that are rarely discussed among users trigger the sparsity problem in tensor factorization. Our solution, cross-domain tensor factorization (CrTF), first determines the topic domain by analyzing communication logs among users using the DBpedia knowledge base and creates a tensor composed of users, other users, and the topics of communication for each domain; it avoids strong bias toward particular domains. It then simultaneously factorizes tensors across domains while integrating semantics from DBpedia into factorizations; this solves the sparsity problem. Experiments using Twitter data sets show that CrTF achieves higher accuracy than the state-of-the-art tensor-based methods and extracts key topics and social influencers for each domain.

Semantic Sensitive Simultaneous Tensor Factorization

The semantics distributed over large-scale knowledge bases can be used to intermediate heterogeneous users’ activity logs created in services; such information can be used to improve applications that can help users to decide the next activities/services. Since user activities can be represented in terms of relationships involving three or more things (e.g. a user tags movie items on a webpage), tensors are an attractive approach to represent them. The recently introduced Semantic Sensitive Tensor Factorization (SSTF) is promising as it achieves high accuracy in predicting users’ activities by basing tensor factorization on the semantics behind objects (e.g. item categories). However, SSTF currently focuses on the factorization of a tensor for a single service and thus has two problems: (1) the balance problem occurs when handling heterogeneous datasets simultaneously, and (2) the sparsity problem triggered by insufficient observations within a single service. Our solution, Semantic Sensitive Simultaneous Tensor Factorization (S\(^3\)TF), tackles the problems by: (1) Creating tensors for individual services and factorizing them simultaneously; it does not force the creation of a tensor from multiple services and factorize the single tensor. This avoids the low prediction accuracy caused by the balance problem. (2) Utilizing shared semantics behind distributed activity logs and assigning semantic bias to each tensor factorization. This avoids the sparsity problem by sharing semantics among services. Experiments using real-world datasets show that S\(^3\)TF achieves higher accuracy in rating prediction than the current best tensor method. It also extracts implicit relationships across services in the feature spaces by simultaneous factorization with shared semantics.

Identifying Novel Topics Based on User Interests

In this chapter, we introduce an agent that builds user interests as a hierarchy of classes where a rating value of the user is assigned to each class and item. The agent measures the similarity of users using user ratings against items as well as those against classes and then generates a user group that has high similarity to the user. Finally, the agent identifies novel topics, those that include new classes that are likely be interesting to the user even though those classes are not present in the user profile. The novel topics for the user are identified by determining a suitable size of the user group and analyzing the items possessed by the users in the user group. Thus, highly accurate recommendation results are guaranteed. Furthermore, our agent presents recommendations with a new measure, score of novelty, so that the user may better understand how novel the recommended items are. By letting the user browse topics against novel items with scores of novelty, we try to expand user interests significantly.

Proposal of Flexible Interface Technology for Multidomain/Multivendor Network Management

The expansion of IP broadband services in recent years have prompted various services on IP network infrastructures to be developed in a short time. We propose flexible system cooperation using ontology-mapping technology based on a semantic concept and interface blending/diagnosis technology using script language in a multidomain/multivendor environment