• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.61-68
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• 2012

Recommender Systems have gained much popularity among researchers and is applied in a number of applications. The exponential growth of users and products poses some key challenges for recommender systems. Recommender Systems mostly suffer from scalability and accuracy. The accuracy of Recommender system is somehow inversely proportional to its scalability. In this paper we proposed a Context Aware Hybrid Recommender System using matrix reduction for Hybrid model and clustering technique for predication of item features. In our approach we used user item-feature rating, User Demographic information and context information i.e. specific time and day to improve scalability and accuracy. Our Algorithm produce better results because we reduce the dimension of items features matrix by using different reduction techniques and use user demographic information, construct context aware hybrid user model, cluster the similar user offline, find the nearest neighbors, predict the item features and recommend the Top N- items.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.721-739
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• 2023

Rating prediction is an important issue in recommender systems, and its accuracy affects the experience of the user and the revenue of the company. Traditional recommender systems use Factorization Machinesfor rating predictions and each feature is selected with the same weight. Thus, there are problems with inaccurate ratings and limited data representation. This study proposes a deep recommendation model based on self-attention Factorization (SAFMR) to solve these problems. This model uses Convolutional Neural Networks to extract features from user and item reviews. The obtained features are fed into self-attention mechanism Factorization Machines, where the self-attention network automatically learns the dependencies of the features and distinguishes the weights of the different features, thereby reducing the prediction error. The model was experimentally evaluated using six classes of dataset. We compared MSE, NDCG and time for several real datasets. The experiment demonstrated that the SAFMR model achieved excellent rating prediction results and recommendation correlations, thereby verifying the effectiveness of the model.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.707-720
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• 2021

To improve the accuracy of the recommendation system, multi-criteria recommendation systems have been widely researched. However, it is highly complicated to extract the preferred features of users and items from the data. To this end, subjective indicators, which indicate a user's priorities for personalized recommendations, should be derived. In this study, we propose a method for generating recommendation candidates by predicting multi-criteria ratings from reviews and using them to derive user priorities. Using a deep learning model based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), multi-criteria prediction ratings were derived from reviews. These ratings were then aggregated to form a linear regression model to predict the overall rating. This model not only predicts the overall rating but also uses the training weights from the layers of the model as the user's priority. Based on this, a new score matrix for recommendation is derived by calculating the similarity between the user and the item according to the criteria, and an item suitable for the user is proposed. The experiment was conducted by collecting the actual "TripAdvisor" dataset. For performance evaluation, the proposed method was compared with a general recommendation system based on singular value decomposition. The results of the experiments demonstrate the high performance of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.357-359
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• 2023

This paper proposes a hybrid recommendation system (RS) model that overcomes the limitations of traditional approaches such as data sparsity, cold start, and scalability by combining collaborative filtering and context-aware techniques. The objective of this model is to enhance the accuracy of recommendations and provide personalized suggestions by leveraging the strengths of collaborative filtering and incorporating user context features to capture their preferences and behavior more effectively. The approach utilizes a novel method that combines contextual attributes with the original user-item rating matrix of CF-based algorithms. Furthermore, we integrate k-mean++ clustering to group users with similar preferences and finally recommend items that have highly rated by other users in the same cluster. The process of partitioning is the use of the rating matrix into clusters based on contextual information offers several advantages. First, it bypasses of the computations over the entire data, reducing runtime and improving scalability. Second, the partitioned clusters hold similar ratings, which can produce greater impacts on each other, leading to more accurate recommendations and providing flexibility in the clustering process. keywords: Context-aware Recommendation, Collaborative Filtering, Kmean++ Clustering.

• KIISE Transactions on Computing Practices
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• v.24 no.2
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• pp.93-98
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• 2018

Recommendation systems are used to provide items of interests for users to maximize a company's profit. Matrix factorization is frequently used by recommendation systems, based on an incomplete user-item rating matrix. However, as the number of items and users increase, it becomes difficult to make accurate recommendations due to the sparsity of data. To overcome this drawback, the use of text data related to items was recently suggested for matrix factorization algorithms. Furthermore, a word-level convolutional neural network was shown to be effective in the process of extracting the word-level features from the text data among these kinds of matrix factorization algorithms. However, it involves a large number of parameters to learn in the word-level convolutional neural network. Thus, we propose a matrix factorization algorithm which utilizes a character-level convolutional neural network with which to extract the character-level features from the text data. We also conducted a performance study with real-life datasets to show the effectiveness of the proposed matrix factorization algorithm.

• Korean Journal of Psychosomatic Medicine
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• v.27 no.2
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• pp.111-118
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• 2019

Objectives : The purpose of this study was to investigate the clinical characteristics of antipsychotic medication prescription for the symptom control in patients with delirium. Methods : One hundred and eighty-five patients referred to consultation-liaison psychiatric services for delirium due to general medical condition were included in this study. All subjects were divided into two groups (antipsychotics users vs. antipsychotics nonusers), and comparison analyses on their clinical characteristics were performed. Results : One hundred and twenty nine patients (66.5%) used antipsychotics for their delirium, and 56 patients (30.3%) did not use antipsychotics. The history of psychotropic medication was more frequently observed in antipsychotic users (5.4% vs. 18.6%, χ²=5.498, p=0.022). Especially, the history of benzodiazepine use was significantly high in antipsychotics users. The total score and sub-items of delirium rating scale-severity items except for the psychomotor retardation item showed higher scores in antipsychotic users than in nonusers (all p<0.05). The total score of the delirium rating scale-diagnosis items was higher in antipsychotic users than in the nonusers (p=0.010). Conclusions : Delirium patients with more severe delirium symptoms and with more history of benzodiazepine use were treated with antipsychotics more frequently than those without. These findings imply that benzodiazepine may not only exacerbate delirium but be associated with aggression or psychomotor agitation that need immediate intervention. Clinicians may need to pay attention not only these external symptoms but also to hypoactive symptoms that may lead to misdiagnosis and undertreatment.

• The Journal of the Society of Stroke on Korean Medicine
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• v.18 no.1
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• pp.77-86
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• 2017

■ Objectives The purpose of this case study is to report the effect of Gami SSanghwa-tang on a patient with central post-stroke pain. ■ Methods The patient was treated with herbal medicine Gami SSanghwa-tang, acupuncture, pharmaco-acupuncture, and moxibustion. The treatment effect was evaluated by Numerical Rating Scale(NRS), Neuropathic Pain Symptom Inventory(NPSI), and 36-item Short-form Health Survey(SF-36). ■ Results After the treatment, the NRS score of pain intensity was reduced from moderate to mild degree. The total NPSI score and subscores also decreased, as the various features of the pain were relieved. The SF-36 score increased, as the patient's quality of life improved. ■ Conclusion This case study suggests that Gami SSanghwa-tang, could be effective in reducing pain and improving quality of life of patients suffering from central post-stroke pain.

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.273-280
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• 2009

Today, many services and products that used to be only provided on offline have been being provided on the web according to the improvement of computing environment and the activation of web usage. These web-based services and products tend to be provided to customer by customer's preferences. This paradigm that considers customer's opinions and features in selecting is called personalization. The related research field is a recommendation. And this recommendation is performed by recommender system. Generally the recommendation is made from the preferences and tastes of customers. And recommender system provides this recommendation to user. However, the recommendation techniques have a couple of problems; they do not provide suitable recommendation to new users and also are limited to computing space that they generate recommendations which is dependent on ratings of products by users. Those problems has gathered some continuous interest from the recommendation field. In the case of new users, so similar users can't be classified because in the case of new users there is no rating created by new users. The problem of the limitation of the recommendation space is not easy to access because it is related to moneywise that the cost will be increasing rapidly when there is an addition to the dimension of recommendation. Therefore, I propose the solution of the recommendation problem of new user and the usage of item quality as weight to improve the accuracy of recommendation in this paper.

• Journal of Oral Medicine and Pain
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• v.31 no.3
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• pp.199-210
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• 2006

The epidemiological researches on the inpatients hospitalized at the oral medicine ward have been continuously carried out since 1970, and most researches have been performed by centering around the oral medicine wards of college hospitals. Numerous specialists have been produced after the establishment of oral medicine, and they have been active in various fields. As dental clinics have gotten bigger, the function of oral medicine in the secondary clinics is being brought out. As admissive channel, case features, case composition and otherwise have not been researched for a long time, the related researches should be carried out from now on. Hereupon, this study was carried out by targeting the 100 inpatients hospitalized at the oral medicine ward of Sun Hospital located in Daejeon Korea, through questionnaire. As the result, the following results were derived. 1. The ages of the inpatients in Sun Hospital were $29.21{\pm}11.31$ on the average; 71 females' mean average was $29.63{\pm}11.29$ and 29 males' mean average was $28.17{\pm}11.48$. In regard of school career, the patients who finished high-school course or higher accounted for 78%; the patients' school career seemed to be relatively high. The patients who complained of temporomandibular pain accounted for the highest proportion with 65%. In motivation to visit this hospital, internet surfing was 11%, mass media was 10%, acquaintance's introduction was 38%. The patients, who were hospitalized at another hospital due to the same symptom, accounted for 56%. The dental clinics, which made the patients visit this hospital, accounted for 20%. The patients, who were previously aware that the present symptom should be treated by oral medicine, accounted for 38%. The patients, who were not aware of the fact in advance, were 62%. The respondents of 51% answered that they were aware of the fact one month or below before hospitalization. 2. The patients, who complained of craniocervical ache, accounted for 58%; the patients, whose ache aches affect dailylife, were 22%. Continuous ache was 14% and intermittent ache was 68%, and dull pain was 23%. 3. Life variations were compared with each other by using SRRS (Social Readjustment Rating Scale). In consequence, the variation within 3 years indicated a significant difference in the both groups but the variation within 6 months did not indicate any differences. 4. In regard of the questionnaire on the incidents happened for a week, the ache-group was compared with the group free from the ache. As the result, the number of strain arisen for a week, the decrease of favorite works and sudden fear indicated a significant difference. Pleasant feeling and the decrease of interests in looks did not indicate a significant difference, but came close to the significance. 5. In the questionnaire on impatience, the ache-group indicated higher value but there was not a significant difference. 6. In the questionnaire on the symptoms caused by stress, the two groups indicated significant differences in the item of 'the teethridge itches and feels a tooth rising' and 'the occiput or the nape is stiff.' In the item 'the inside of the cheek or the teethridge are widely peeled off, accompanied with ache and hemorrhage', 'the face has acne or pimple' and 'headache frequently attacks', a significant difference was not observed but the two groups came close to the significance.