Jin-Guk Jung

Location-based service with context data for a restaurant recommendation

Utilizing Global Positioning System (GPS) technology, it is possible to find and recommend restaurants for users operating mobile devices. For recommending restaurants, Personal Digital Assistants or cellular phones only consider the location of restaurants. However, a users background and environment information is assumed to be directly related to recommendation quality. In this paper, therefore, a recommender system using context information and a decision tree model for efficient recommendation is presented. This system considers location context, personal context, environment context, and user preference. Restaurant lists are obtained from location context, personal context, and environment context using the decision tree model. In addition, a weight value is used for reflecting user preferences. Finally, the system recommends appropriate restaurants to the mobile user. For this experiment, performance was verified using measurements such as k-fold cross-validation and Mean Absolute Error. As a result, the proposed system obtained an improvement in recommendation performance.

Leveraging personal photos to inferring friendships in social network services

Social network services have become widely used as an important tool to share rich information; in such networks, making new friends is the most basic functionality to enable users to take advantage of their social networks. In this paper we look into personal photos as an additional source for social network analysis and analyze the potential of people tags in the photos for friend recommendations. We also propose a new compact data structure, collectively called Face Co-Occurrence Networks (FCON), which stores crucial and quantitative information about peoples appearance in photos. We discover strong associative relationships among people and recommend reliable social friends by utilizing FCON. Experimental results demonstrate the effectiveness and efficiency of our method for recommending friends in social network services.

User preference modeling from positive contents for personalized recommendation

With the spread of the Web, users can obtain a wide variety of information, and also can access novel content in real time. In this environment, finding useful information from a huge amount of available content becomes a time consuming process. In this paper, we focus on user modeling for personalization to recommend content relevant to user interests. Techniques used for association rules in deriving user profiles are exploited for discovering useful and meaningful patterns of users. Each user preference is presented the frequent term patterns, collectively called PTP (Personalized Term Pattern) and the preference terms, called PT (Personalized Term). In addition, a content-based filtering approach is employed to recommend content corresponding with user preferences. In order to evaluate the performance of the proposed method, we compare experimental results with those of a probabilistic learning model and vector space model. The experimental evaluation on NSF research award datasets demonstrates that the proposed method brings significant advantages in terms of improving the recommendation quality in comparison with the other methods.

Associative face co-occurrence networks for recommending friends in social networks

In social network services, which have become widely used as an important tool to share rich information, making new friends is the most basic functionality to enable users to take advantage of their social networks. However, in current social network services, making new friends still relies on manually browsing networks of current friends. Even though the most services try to automatically suggest new friends, users can hardly accept those suggestions without any meaningful explanation of relationships. To deal with this issue, in this paper, we look into personal photos as an additional source for social network analysis and analyze the potential of name tagging in the photos for applying to friend recommendations. Moreover, we propose a new compact data structure, namely Face Co-Occurrence Networks (FCON), for photo networks storing crucial and quantitative information about people appearance in photos. By incorporating with FCON, we discover strong associative relationships among people and recommend reliable social friends. Experimental results demonstrate the feasibility of our method for recommending friends in social network services.

U2Mind: visual semantic relationships query for retrieving photos in social network

This research is to investigate a method that enables social networks to provide a semi-automatic system. The system will allow users to organize their target photos, using the concept of ownership attributes that describe the relationships between objects in the photos. In this paper, we propose formulating a visual semantic relationships query for photo retrieval. A Visual Semantic Relationship Query interface helps users describe their perspectives about the desired photo in a semantic manner. In the ranking process, by interpreting both concepts and relationships, a users query is transformed into a SPARQL, which is then sent to the JOSEKI server, and the returned photos are evaluated in terms of relevance to each photo. The experimental results demonstrate the effectiveness of the proposed system.

Discovering Frequent Patterns by Constructing Frequent Pattern Network over Data Streams in E-Marketplaces

The extracting useful information such as itemsets and frequent patterns from the data becomes very important in terms of marketing strategies and maximizing profit in e-marketplaces. Although existing algorithms mining frequent patterns from the data are useful for persistent databases, they have some limitations of data mining from dynamic data arising from the continuous, unbounded and high speed characteristics of data streams. To identify useful frequent patterns in data streams, this paper proposes a frequent pattern network and a new method for discovering frequent patterns through the approximation of frequency counting on the network. The frequent pattern network, whose vertices and edges represent summarized information of transaction data, provides a user-centered environment based on the process of continuously mining frequent patterns because the proposed network is a small and compact data structure, and flexible for minimum support value. The experimental results show that proposed method is more efficient than FP-growth and Apriori methods, and the discussion of memory usage demonstrates the efficiency of the proposed method.

Extracting Relations towards Ontology Extension

Extracting local ontology from domain-specific documents for the purpose of acquiring knowledge or semantic information to extend their ontologies is considered very important. Main components of ontology are concepts and relations between concepts. In this paper, we focus on extracting triples, in which verbs are relations and subjects/objects are concepts, from documents based on natural language. Further, we show that term frequency is the most reliable measure among tf-idf and entropy on evaluating relations extracted from documents, particularly the aircraft maintenance manual.

EXPERTS SEARCH AND RANK WITH SOCIAL NETWORK: AN ONTOLOGY-BASED APPROACH

Experts finding, one of the most important tasks in social networks, is aimed at identifying individuals with relevant expertise or experience in a given topic. Several approaches have been proposed for finding experts in social networks from documents or web repositories. However, the semantic approach for modeling the information to find experts has not yet been explored. In this paper, we propose a novel method to index the academic information in an ontology-based model for finding and ranking the experts in a particular domain. Additionally, we propose an effective method to construct the academic social network by exploring the relations among the experts and measuring the score of each expert. The score of an expert is measured considering the contributions of relevant publications and relationships among other expert candidates. It is very efficient to find and ranking experts to take advantage of the millions of candidate experts being with relationships. An experiment conducted to evaluate our model shows that experts finding and ranking with an ontological approach integrated with the social network is more effective than other approaches.

Building a Semantic Social Network Based on Interpersonal Relationships

With the emergence of the Smart phone, people can use Online Social Network Services ubiquitously, leading to a significant increase of the number of participants in online social networks. Under these circumstances, online users will require an intelligent and intuitive social relationship management system such as the ontology-driven browsing method. In this paper, to build a user-centered semantic social network and to represent entities and relationships with ontology to improve retrieval performance of the semantic social network, we will design our ontology extended from FOAF, RELATIONSHIP and propose a new method to compute closeness among friends using resources on social networks. Furthermore, we evaluate our ontology-driven browsing on via implementing a prototype system.

A Context-Awareness for Mechanical Maintenance

Ongoing development of information technology provides a fundamental environment of maintaining vast manual information in digital form. Demand of digital information increases because of easy handling and presentation of documents on various structure manner. As a result, technical documents like mechanical device maintenance manuals have numerous advantages to move from page version into electronic edition. However, it is still difficult for context-aware systems to provide information relevant to specific context because the information scatters in multiple parts of the document. The object detection techniques of context-aware systems could not recognize all objects completely. In this paper, we propose a context-aware system which can recommends relevant information through analyzing context delivered by a vedio device located in workplace around. To compare the current context with documents, three metrics are used for computing similarities between contextual information and keywords extracted from documents. For our experiments, we preprocessed hundreads of TASKs in the aircrafts maintenance manual and made several cases for context. Our experiments showed that our proposing system could provide information related with context.