• Journal of Information Processing Systems
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• v.18 no.2
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• pp.245-257
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• 2022

In the Internet of Things (IoT) era, the types of devices used by one user are becoming more diverse and the number of devices is also increasing. However, a forensic investigator is restricted to exploit or collect all the user's devices; there are legal issues (e.g., privacy, jurisdiction) and technical issues (e.g., computing resources, the increase in storage capacity). Therefore, in the digital forensics field, it has been a challenge to acquire information that remains on the devices that could not be collected, by analyzing the seized devices. In this study, we focus on the fact that multiple devices share data through account synchronization of the online platform. We propose a novel way of identifying the user's interest through analyzing the remnants of targeted advertising which is provided based on the visited websites or search terms of logged-in users. We introduce a detailed methodology to pick out the targeted advertising from cache data and infer the user's interest using deep learning. In this process, an improved learning model considering the unique characteristics of advertisement is implemented. The experimental result demonstrates that the proposed method can effectively identify the user interest even though only one device is examined.

• Journal of Information Technology Applications and Management
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• v.25 no.4
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• pp.145-156
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• 2018

This study proposes a content-based recommendation algorithm (NRA) for pet food. The proposed algorithm tries to recommend appropriate or inappropriate feed by using collective intelligence based on user experience and prior knowledge of experts. Based on the physical and health status of the dogs, this study suggests what kind of nutrients are necessary for the dogs and the most recommended pet food containing these nutrients. Performance evaluation was performed in terms of recall, precision, F1 and AUC. As a result of the performance evaluation, the AUC and F1 value of the proposed NRA was 15% and 42% higher than that of the baseline model, respectively. In addition, the performance of NRA is shown higher for recommendation of normal dogs than disease dogs.

• Journal of the Korea Convergence Society
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• v.9 no.12
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• pp.25-32
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• 2018

In the massive online fashion market, it is not easy for consumers to find the fashion style they want by keyword search for their preferred style. It can be resolved into consumer needs based fashion recommendation. Most of the existing online shopping sites have collected cumtomer's preference style using the online quastionnair. In this paper, we propose a simple but effective novel model that resolve the traditional method in fashion profiling for consumer's preference style and needs using implicit profiling method. In addition, we proposed a learning model that reflects the characteristics of the images itself through the deep learning-based intelligent preferred fashion model learned from the collected data. We show that the proposed model gave meaningful results through the qualitative evaluation.

• Journal of the Korea Convergence Society
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• v.11 no.7
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• pp.1-7
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• 2020

As the performance of mobile devices has dramatically improved, users can perform many tasks in a mobile environment. This means that the use of behavior information stored in the mobile device can tell a lot of users. For example, a user's text message and frequently used application information (behavioral information) can be utilized to create useful information, such as whether the user is interested in parenting(profile prediction). In this study, I investigate the behavior information of the user that can be collected in the mobile device and propose the item that can profile the user. And I also suggest ideas about how to utilize profiling information.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.21-41
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• 2010

This paper proposes a system that can serve users with appropriate search results through real time filtering, and implemented adaptive user profiling based personalized information retrieval system(PIRS) using users' implicit feedbacks in order to deal with the problem of existing search systems such as Google or MSN that does not satisfy various user' personal search needs. One of the reasons that existing search systems hard to satisfy various user' personal needs is that it is not easy to recognize users' search intentions because of the uncertainty of search intentions. The uncertainty of search intentions means that users may want to different search results using the same query. For example, when a user inputs "java" query, the user may want to be retrieved "java" results as a computer programming language, a coffee of java, or a island of Indonesia. In other words, this uncertainty is due to ambiguity of search queries. Moreover, if the number of the used words for a query is fewer, this uncertainty will be more increased. Real-time filtering for search results returns only those results that belong to user-selected domain for a given query. Although it looks similar to a general directory search, it is different in that the search is executed for all web documents rather than sites, and each document in the search results is classified into the given domain in real time. By applying information filtering using real time directory classifying technology for search results to personalization, the number of delivering results to users is effectively decreased, and the satisfaction for the results is improved. In this paper, a user preference profile has a hierarchical structure, and consists of domains, used queries, and selected documents. Because the hierarchy structure of user preference profile can apply the context when users perfomed search, the structure is able to deal with the uncertainty of user intentions, when search is carried out, the intention may differ according to the context such as time or place for the same query. Furthermore, this structure is able to more effectively track web documents search behaviors of a user for each domain, and timely recognize the changes of user intentions. An IP address of each device was used to identify each user, and the user preference profile is continuously updated based on the observed user behaviors for search results. Also, we measured user satisfaction for search results by observing the user behaviors for the selected search result. Our proposed system automatically recognizes user preferences by using implicit feedbacks from users such as staying time on the selected search result and the exit condition from the page, and dynamically updates their preferences. Whenever search is performed by a user, our system finds the user preference profile for the given IP address, and if the file is not exist then a new user preference profile is created in the server, otherwise the file is updated with the transmitted information. If the file is not exist in the server, the system provides Google' results to users, and the reflection value is increased/decreased whenever user search. We carried out some experiments to evaluate the performance of adaptive user preference profile technique and real time filtering, and the results are satisfactory. According to our experimental results, participants are satisfied with average 4.7 documents in the top 10 search list by using adaptive user preference profile technique with real time filtering, and this result shows that our method outperforms Google's by 23.2%.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.117-140
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• 2019

As the aging phenomenon accelerates and various social problems related to the elderly of the vulnerable are raised, the need for effective elderly care solutions to protect the health and safety of the elderly generation is growing. Recently, more and more people are using Smart Toys equipped with ICT technology for care for elderly. In particular, log data collected through smart toys is highly valuable to be used as a quantitative and objective indicator in areas such as policy-making and service planning. However, research related to smart toys is limited, such as the development of smart toys and the validation of smart toy effectiveness. In other words, there is a dearth of research to derive insights based on log data collected through smart toys and to use them for decision making. This study will analyze log data collected from smart toy and derive effective insights to improve the quality of life for elderly users. Specifically, the user profiling-based analysis and elicitation of a change in quality of life mechanism based on behavior were performed. First, in the user profiling analysis, two important dimensions of classifying the type of elderly group from five factors of elderly user's living management were derived: 'Routine Activities' and 'Work-out Activities'. Based on the dimensions derived, a hierarchical cluster analysis and K-Means clustering were performed to classify the entire elderly user into three groups. Through a profiling analysis, the demographic characteristics of each group of elderlies and the behavior of using smart toy were identified. Second, stepwise regression was performed in eliciting the mechanism of change in quality of life. The effects of interaction, content usage, and indoor activity have been identified on the improvement of depression and lifestyle for the elderly. In addition, it identified the role of user performance evaluation and satisfaction with smart toy as a parameter that mediated the relationship between usage behavior and quality of life change. Specific mechanisms are as follows. First, the interaction between smart toy and elderly was found to have an effect of improving the depression by mediating attitudes to smart toy. The 'Satisfaction toward Smart Toy,' a variable that affects the improvement of the elderly's depression, changes how users evaluate smart toy performance. At this time, it has been identified that it is the interaction with smart toy that has a positive effect on smart toy These results can be interpreted as an elderly with a desire to meet emotional stability interact actively with smart toy, and a positive assessment of smart toy, greatly appreciating the effectiveness of smart toy. Second, the content usage has been confirmed to have a direct effect on improving lifestyle without going through other variables. Elderly who use a lot of the content provided by smart toy have improved their lifestyle. However, this effect has occurred regardless of the attitude the user has toward smart toy. Third, log data show that a high degree of indoor activity improves both the lifestyle and depression of the elderly. The more indoor activity, the better the lifestyle of the elderly, and these effects occur regardless of the user's attitude toward smart toy. In addition, elderly with a high degree of indoor activity are satisfied with smart toys, which cause improvement in the elderly's depression. However, it can be interpreted that elderly who prefer outdoor activities than indoor activities, or those who are less active due to health problems, are hard to satisfied with smart toys, and are not able to get the effects of improving depression. In summary, based on the activities of the elderly, three groups of elderly were identified and the important characteristics of each type were identified. In addition, this study sought to identify the mechanism by which the behavior of the elderly on smart toy affects the lives of the actual elderly, and to derive user needs and insights.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.400-409
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• 2003

Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, SOM (Self-Organizing Map) and CBR (Case Based Reasoning) by changing an unsupervised clustering problem into a supervised user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference.

• Journal of Digital Forensics
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• v.12 no.3
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• pp.97-108
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• 2018

Computer technology and Internet advances enable easy massive file transfer by messenger, email, and web hard service users this means that a child pornography file owner who is illegally possessing itself can quickly transfer that to other users However there are currently no specific ways to prevent or block the distribution of child pornography between messenger, email, and web hard service users. in this paper, we propose a method to prevent the distribution of child pornography using the MD5, SHA-1 hash value stored in the 'police Child pornography Profiling system' and to identify the child pornography suspects using the subscriber information. the user extracts the hash value of the file before distributing the file, compared it with police system, and if it has the same value, blocks the transmission of the file and sends warning to the owner. the service provider sends the subscriber information to investigation agency child pornography owners can conduct a quick and accurate investigation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.690-696
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• 2016

Search engines that rely on morphological matching of user query and web document content do not support individual interests. This research proposes a personalized web search scheme that returns the results that reflect the users' query intent and personal preferences. The performance of the personalized search depends on using an effective user profiling strategy to accurately capture the users' personal interests. In this study, the user profiles are the databases of topic words and customized weights based on the recent user queries and the frequency of topic words in click history. To determine the precise meaning of ambiguous queries and topic words, this strategy uses WordNet to calculate the semantic relatedness to words in the user profile. The experiments were conducted by installing a query expansion and re-ranking modules on the general web search systems. The results showed that this method has 92% precision and 82% recall in the top 10 search results, proving the enhanced performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.3
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• pp.587-594
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• 2019

This paper proposes a personalized web search technique that produces ranked results reflecting user's query intents and individual interests. The performance of personalized search relies on an effective users' profiling strategy to accurately capture their interests and preferences. User profile is a data set of words and customized weights based on recent user queries and the topic words of web documents from their click history. Personal profile is used to expand a user query to the personalized query before the web search. To determine the exact meaning of ambiguous queries and topic words, this strategy uses WordNet to calculate semantic similarities to words in the user personal profile. Experimental results with query expansion and re-ranking modules installed on general search systems shows enhanced performance with this personalized search technique in terms of precision and recall.