Showing papers by "Geun-Sik Jo published in 2011"

Collaborative error-reflected models for cold-start recommender systems

Abstract: Collaborative Filtering (CF), one of the most successful technologies among recommender systems, is a system assisting users to easily find useful information. One notable challenge in practical CF is the cold start problem, which can be divided into cold start items and cold start users. Traditional CF systems are typically unable to make good quality recommendations in the situation where users and items have few opinions. To address these issues, in this paper, we propose a unique method of building models derived from explicit ratings and we apply the models to CF recommender systems. The proposed method first predicts actual ratings and subsequently identifies prediction errors for each user. From this error information, pre-computed models, collectively called the error-reflected model, are built. We then apply the models to new predictions. Experimental results show that our approach obtains significant improvement in dealing with cold start problems, compared to existing work.

Collaborative user modeling with user-generated tags for social recommender systems

Abstract: With the popularity of social media services, the sheer amount of content is increasing exponentially on the Social Web that leads to attract considerable attention to recommender systems. Recommender systems provide users with recommendations of items suited to their needs. To provide proper recommendations to users, recommender systems require an accurate user model that can reflect a user's characteristics, preferences and needs. In this study, by leveraging user-generated tags as preference indicators, we propose a new collaborative approach to user modeling that can be exploited to recommender systems. Our approach first discovers relevant and irrelevant topics for users, and then enriches an individual user model with collaboration from other similar users. In order to evaluate the performance of our model, we compare experimental results with a user model based on collaborative filtering approaches and a vector space model. The experimental results have shown the proposed model provides a better representation in user interests and achieves better recommendation results in terms of accuracy and ranking.

Collaborative user modeling for enhanced content filtering in recommender systems

Abstract: Recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of content suited to their needs. To provide proper recommendations to users, personalized recommender systems require accurate user models of characteristics, preferences and needs. In this study, we propose a collaborative approach to user modeling for enhancing personalized recommendations to users. Our approach first discovers useful and meaningful user patterns, and then enriches the personal model with collaboration from other similar users. In order to evaluate the performance of our approach, we compare experimental results with those of a probabilistic learning model, a user model based on collaborative filtering approaches, and a vector space model. We present experimental results that show how our model performs better than existing alternatives.

Automatic emotion annotation of movie dialogue using WordNet

Abstract: With the increasing interest in multimedia annotation, emotion annotation is being recognized as an essential resource which can be applied for a variety of purposes, including video information retrieval and dialogue systems. Here we introduce an automatic emotion annotation schema for dialogues, which could be used for the retrieval of specific scenes in film. Distinguished from previous works, we apply a new approach using the hypernym/hyponym relations and synonyms of WordNet, which enables us to organize a novel set of emotional concepts and to automatically detect whether a specific emotional word is associated with a specified emotional concept through measuring the conceptual distance between them.

Fuzzy Ontology Integration Using Consensus to Solve Conflicts on Concept Level

Abstract: Nowadays, ontology has been backbone of Semantic web. However, the current ontologies are based on traditional logic such as first-order logic and description logic. The conceptual formalism of the ontologies cannot be fully representative for imprecise and vague information (e.g. ”rainfall is very heavy”) in many application domains. In this paper, a domain fuzzy ontology is defined clearly, and its components such as fuzzy relation, concrete fuzzy concept, and fuzzy domain concept as well as similarity measures between the components are addressed. Fuzzy ontology integration on concept level using consensus method to solve conflicts among the ontologies is proposed. In particular, the postulates for integration are specified and algorithms for reconciling conflicts among fuzzy concepts in ontology integration are proposed.

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

Abstract: 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 user's 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.

Photo search in a personal photo diary by drawing face position with people tagging

Abstract: In recent years, people tend to maintain personal photos in digital spaces not only to share their experiences with social friends but also to jog their own memory. Therefore, an effective solution is crucial to the growth of the needs for recording one's daily life. In this study, we have developed a complete system for personal photo diary system, namely MePTroy. With a friendly user interface, users can easily maintain personal episodes and memories with photos. In addition, we also support a flexible method for photo search based on the position of facial appearance that enables users to access episodes quickly. By integrating face detection and recognition technologies, as well as a friendly UI, MePTory offers diverse functionalities to annotate and search photos.

A Collaborative Video Annotation and Browsing System using Linked Data

Abstract: Previously common users just want to watch the video contents without any specific requirements or purposes. However, in today's life while watching video user attempts to know and discover more about things that appear on the video. Therefore, the requirements for finding multimedia or browsing information of objects that users want, are spreading with the increasing use of multimedia such as videos which are not only available on the internet-capable devices such as computers but also on smart TV and smart phone. In order to meet the users. requirements, labor-intensive annotation of objects in video contents is inevitable. For this reason, many researchers have actively studied about methods of annotating the object that appear on the video. In keyword-based annotation related information of the object that appeared on the video content is immediately added and annotation data including all related information about the object must be individually managed. Users will have to directly input all related information to the object. Consequently, when a user browses for information that related to the object, user can only find and get limited resources that solely exists in annotated data. Also, in order to place annotation for objects user's huge workload is required. To cope with reducing user's workload and to minimize the work involved in annotation, in existing object-based annotation automatic annotation is being attempted using computer vision techniques like object detection, recognition and tracking. By using such computer vision techniques a wide variety of objects that appears on the video content must be all detected and recognized. But until now it is still a problem facing some difficulties which have to deal with automated annotation. To overcome these difficulties, we propose a system which consists of two modules. The first module is the annotation module that enables many annotators to collaboratively annotate the objects in the video content in order to access the semantic data using Linked Data. Annotation data managed by annotation server is represented using ontology so that the information can easily be shared and extended. Since annotation data does not include all the relevant information of the object, existing objects in Linked Data and objects that appear in the video content simply connect with each other to get all the related information of the object. In other words, annotation data which contains only URI and metadata like position, time and size are stored on the annotation sever. So when user needs other related information about the object, all of that information is retrieved from Linked Data through its relevant URI. The second module enables viewers to browse interesting information about the object using annotation data which is collaboratively generated by many users while watching video. With this system, through simple user interaction the query is automatically generated and all the related information is retrieved from Linked Data and finally all the additional information of the object is offered to the user. With this study, in the future of Semantic Web environment our proposed system is expected to establish a better video content service environment by offering users relevant information about the objects that appear on the screen of any internet-capable devices such as PC, smart TV or smart phone.

An ontology based model for experts search and ranking

Abstract: Experts finding is an important issue for finding potential contributors or expertise in a specific field. In scientific research, researchers often try to find an experts list related to their interest areas to acquire the knowledge about state arts of current research and novices can get benefit to find new ideas for research. In this paper, we proposed an ontological model to find and rank the experts in a particular domain. First, an Academic Knowledge Base(AKB) is built for a particular domain and then an academic social network (ASN) is constructed based on the information provided by the knowledge base for a given topic. In our approach, we proposed a cohesive modeling approach to investigate academic information considering heterogeneous relationship. Our proposed model provides a novel approach to organize and manage the real world academic information in a structural way which can share and reuse by others. Based on this structured academic information an academic social network is built to find the experts for a particular topic. Moreover, the academic social network ranks the experts with a ranking scores depending upon relationships among expert candidates. Finally, we verify the experimental evaluations of our model which improve precision of finding experts compare to baseline methods.

Ontology-Driven Visualization System for Semantic Search

Abstract: -Technical manuals are very diverse, ranging from manuals on software to manuals on commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of manual information finding the necessary information is quite difficult. In case of electronic maintenance manuals currently used by companies, mechanics should search for the related information to accomplish their tasks. And it is difficult to grasp relationships among contents in manuals. Search process is time-consuming and laborious for mechanics. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we model new ontology to represent and retrieve contents of manuals and design the visualization system based on proposed ontology. To model ontology, we analyzed aircraft maintenance process, extracted concepts and defined relationships between concepts. After modeling, we created instances of each class using technical manuals. Our system visualizes related information so that mechanics can intuitively grasp the information. This allows workers to easily get information for given tasks and to reduce their time to search related information. Also, related information can be understood at a time through visualization.

Anchor-Prior: An effective algorithm for ontology integration

Abstract: Most previous studies of ontology integration have simply involved blind or exhaustive matching among all concepts across ontologies. Therefore, the computational complexity of integrating two ontologies is O(n2). In addition, semantic mismatches, logical inconsistencies and conceptual conflicts in ontology integration have not yet become avoidable. The main contribution of the approach presented here is to reduce the computational complexity and to enhance the accuracy of ontology integration. The key idea of this approach is to start from an Anchor (two matched concepts) to work towards a collection of matched pairs among its neighboring concepts by computing similarities between the “priorly” collected concepts across the ontologies starting from the anchor. The “priorly” means that the PMC, which provides additional suggestions for possible matching concepts, is used to determine for which concepts the similarity should be priorly computed. The algorithm proposed here, based on the idea described above, is called Anchor-Prior algorithm. Experimental comparisons of computational complexity and accuracy with previous approaches are carried out. The results show that the proposed algorithm is effective in terms of both performance (computational time O(n*logn)) and accuracy by avoiding an exponential increase in the number of unmatchable concepts to be checked and by reducing concept mismatches.

Semantic Interaction TV Based on Spatial Relations and Ontology

Abstract: We propose a novel T-commerce model based on semantic interaction, as the TV broadcasting environment is moving to interactive digital TV such as IPTV, Hybrid TV, and Smart TV. The purpose of our work is to design and implement the system of our new T-commerce model. Thus, our system applies spatial relation rules and ontology for object recognition if a viewer is interested in one on the screen of TV program such as drama, movie, and news. All object information needs to be annotated in a TV program to identify the user selection. However, the effort must be reduced, as it costs too much. The program can be identified as an annotated item, a combination of known base area and spatial relations to the user selection, even though it does not have the exact position. The TV system can semantically interact with the user via this combination. A prototype system, semantic interaction TV, has been implemented to achieve this semantic interaction. Our case studies show that the proposed system is useful to T-commerce.

A Semantic Annotation Method for Efficient Representation of Moving Objects

Abstract: Recently, researches for semantic annotation methods which represent and search objects included in video data, have been briskly activated since video starts to be popularized as types for interactive contents. Different location data occurs at each frame because coordinates of moving objects are changed with the course of time. Saving the location data for objects of every frame is too ineffective. Thus, it is needed to compress and represent effectively. This paper suggests two methods; the first, ontology modeling for moving objects to make users intuitively understandable for the information, the second, to reduce the amount of data for annotating moving objects by using cubic spline interpolation. To verify efficiency of the suggested method, we implemented the interactive video system and then compared with each video dataset based on sampling intervals. The result follows : when we got samples of coordinate less than every 15 frame, it showed that could save up to 80% amount of data storage; moreover, maximum of error deviation was under 31 pixels and the average was less than 4 pixels.

A Context-Awareness for Mechanical Maintenance

Abstract: 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 aircraft's maintenance manual and made several cases for context. Our experiments showed that our proposing system could provide information related with context.

GGenre Pattern based User Clustering for Performance Improvement of Collaborative Filtering System

Abstract: Collaborative filtering system is the clustering about user is built and then based on that clustering results will recommend the preferred item to the user. However, building user clustering is time consuming and also once the users evaluate and give feedback about the film then rebuilding the system is not simple. In this paper, genre pattern of movie recommendation systems is being used and in order to simplify and reduce time of rebuilding user clustering. A Frequent pattern networks is used and then extracts user preference genre patterns and through that extracted patterns user clustering will be built. Through built the clustering for all neighboring users to collaborative filtering is applied and then recommends movies to the user. When receiving user information feedback, traditional collaborative filtering is to rebuild the clustering for all neighbouring users to research and do the clustering. However by using frequent pattern Networks, through user clustering based on genre pattern, collaborative filtering is applied and when rebuilding user clustering inquiry limited by search time can be reduced. After receiving user information feedback through proposed user clustering based on genre pattern, the time that need to spent on re-establishing user clustering can be reduced and also enable the possibility of traditional collaborative filtering systems and recommendation of a similar performance.