• The Journal of Information Systems
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• v.26 no.3
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• pp.149-169
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• 2017

Purpose The purpose of this study is to investigate how OTA recommendation influences users' purchase decision making and reuse intention based on the users' destination type. And we compare the results of domestic destination and overseas destination. Design/methodology/approach This research model was designed with the recommendation elements of OTA. And this study conducted an empirical analysis using self-administered questionnaires. The target of the analysis is an individual who has purchased hotel rooms through the OTA for the past one year. A total of 374 usable data were collected (177 domestic respondents and 197 overseas respondents) and analyzed using partial least squares analysis using Smart-PLS 3.0. Findings Two OTA recommendation characteristics - recommendation accuracy and recommendation objectivity were significant in overall model. And easy of decision making was significantly affect to OTA reuse intention. Also, only recommendation accuracy variable was revealed to significant moderating variable between domestic model and overseas model.

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.31-42
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• 2020

During the 4th Industrial Revolution, service platforms utilizing diverse contents are emerging, and research on recommended systems that can be customized to users to provide quality service is being conducted. hybrid recommendation systems that provide high accuracy recommendations are being researched in various domains, and various filtering techniques, machine learning, and deep learning are being applied to recommended systems. However, in a recommended service environment where data must be analyzed and processed real time, the accuracy of the recommendation is important, but the computational speed is also very important. Due to high level of model complexity, a hybrid recommendation system or a Deep Learning-based recommendation system takes a long time to calculate. In this paper, a Cascade-hybrid recommended algorithm is proposed that can reduce the computational time while maintaining the accuracy of the recommendation. The proposed algorithm was designed to reduce the complexity of the model and minimize the computational speed while processing sequentially, rather than using existing weights or using a hybrid recommendation technique handled in parallel. Therefore, through the algorithms in this paper, contents can be analyzed and recommended effectively and real time through services such as SNS environments or shared economy platforms.

• Smart Media Journal
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• v.9 no.1
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• pp.45-50
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• 2020

With the development of deep learning, studies using artificial neural networks based on deep learning in recommendation systems are being actively conducted. Especially, the recommendation system based on RNN (Recurrent Neural Network) shows good performance because it considers the sequential characteristics of data. This study proposes a travel route recommendation system using GRU(Gated Recurrent Unit) and Session-based Parallel Mini-batch which are RNN-based algorithm. This study improved the recommendation performance through an ensemble of top1 and bpr(Bayesian personalized ranking) error functions. In addition, it was confirmed that the RNN-based recommendation system considering the sequential characteristics in the data makes a recommendation reflecting the meaning of the travel destination inherent in the travel route.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3266-3285
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• 2023

With the popularity of online learning, intelligent tutoring systems are starting to become mainstream for assisting online question practice. Surrounded by abundant learning resources, some students struggle to select the proper questions. Personalized question recommendation is crucial for supporting students in choosing the proper questions to improve their learning performance. However, traditional question recommendation methods (i.e., collaborative filtering (CF) and cognitive diagnosis model (CDM)) cannot meet students' needs well. The CDM-based question recommendation ignores students' requirements and similarities, resulting in inaccuracies in the recommendation. Even CF examines student similarities, it disregards their knowledge proficiency and struggles when generating questions of appropriate difficulty. To solve these issues, we first design an enhanced cognitive diagnosis process that integrates students' affection into traditional CDM by employing the non-compensatory bidimensional item response model (NCB-IRM) to enhance the representation of individual personality. Subsequently, we propose an affection-enhanced personalized question recommendation (AE-PQR) method for online learning. It introduces NCB-IRM to CF, considering both individual and common characteristics of students' responses to maintain rationality and accuracy for personalized question recommendation. Experimental results show that our proposed method improves the accuracy of diagnosed student cognition and the appropriateness of recommended questions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3050-3063
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• 2023

Recommendation systems provide personalized products or services to online users by mining their past preferences. Collaborative filtering is a popular recommendation technique because it is easy to implement. However, with the rapid growth of the number of users in recommendation systems, collaborative filtering suffers from serious scalability and sparsity problems. To address these problems, a novel collaborative filtering recommendation algorithm is proposed. The proposed algorithm partitions the users using affinity propagation clustering, and searches for k nearest neighbors in the partition where active user belongs, which can reduce the range of searching and improve real-time performance. When predicting the ratings of active user's unrated items, mean deviation method is used to impute values for neighbors' missing ratings, thus the sparsity can be decreased and the recommendation quality can be ensured. Experiments based on two different datasets show that the proposed algorithm is excellent both in terms of real-time performance and recommendation quality.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.11
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• pp.475-482
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• 2013

Several approaches to recommendation systems have been studied. One of the most successful technologies for building personalization and recommendation systems is collaborative filtering, which is a technique that provides a process of filtering customer information based on such information profiles. Collaborative filtering systems, however, have a sparsity if there is not enough data to recommend. In this paper, we suggest a movie recommendation system, based on the weighted personal propensity and the collaborating filtering system, in order to provide a solution to such sparsity. Furthermore, we assess the system's applicability by using the open database MovieLens, and present a weighted personal propensity framework for improvement in the performance of recommender systems. We successfully come up with a movie recommendation system through the optimal personalization factors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.58-64
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• 2005

Customer sensibility based recommendation agent system was developed to tailor to the customer the suggestion of goods and the description of store catalog in on-line shopping mall. The recommendation agent system composed of five modules and seven services including specialized algorithm. This study was to investigate the effectiveness of the customer sensibility based recommendation agent system in on-line shopping mall. This study asked 30 male and female students to perform the task in on-line shopping mall and facilitated them questionnaires. The questionnaires were administered to subjects to measure quality precision, ease of use, support of buying, purchasing power, future intention of the system. The study revealed that good part of the subjects positively evaluated the customer sensibility based recommendation system except for ease of use. The study on usability of the recommendation agent system has need to be performed in next. This paper shows that the satisfaction and the buying power of customers may be improved by presenting customer sensibility based recommendation in on-line shopping mall.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.1
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• pp.25-40
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• 2023

Recommendation systems are frequently used by users to generate intelligent automatic decisions. In the study of movie recommendation system, the existing approach uses largely collaboration and content-based filtering techniques. Collaborative filtering considers user similarity, while content-based filtering focuses on the activity of a single user. Also, mixed filtering approaches that combine collaborative filtering and content-based filtering are being used to compensate for each other's limitations. Recently, several AI-based similarity techniques have been used to find similarities between users to provide better recommendation services. This paper aims to provide the prospective expansion by deriving possible solutions through the analysis of various existing movie recommendation systems and their challenges.

• Asia pacific journal of information systems
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• v.29 no.2
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• pp.203-216
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• 2019

There is an accuracy-diversity dilemma with personalized recommendation services. Some researchers believe that accurate recommendations might reinforce customer satisfaction. However, others claim that highly accurate recommendations and customer satisfaction are not always correlated. Thus, this study attempts to establish the causal factors that determine customer satisfaction with personalized recommendation services to reconcile these incompatible views. This paper employs statistical analyses of simulation to investigate an accuracy-diversity dilemma with personalized recommendation services. To this end, we develop a personalized recommendation system and measured accuracy, diversity, and customer satisfaction using a simulation method. The results show that accurate recommendations positively affected customer satisfaction, whereas diverse recommendations negatively affected customer satisfaction. Also, customer satisfaction was associated with the recommendation product size when neighborhood size was optimal in accuracy. Thus, these results offer insights into personalizing recommendation service providers. The providers must identify customers' preferences correctly and suggest more accurate recommendations. Furthermore, accuracy is not always improved as the number of product recommendation increases. Accordingly, providers must propose adequate number of product recommendation.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.167-176
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• 2020

Recommendation Systems is the top requirements for many people and researchers for the need required by them with the proper suggestion with their personal indeed, sorting and suggesting doctor to the patient. Most of the rating prediction in recommendation systems are based on patient's feedback with their information regarding their treatment. Patient's preferences will be based on the historical behaviour of similar patients. The similarity between the patients is generally measured by the patient's feedback with the information about the doctor with the treatment methods with their success rate. This paper presents a new method of predicting Top Ranked Doctor's in recommendation systems. The proposed Recommendation system starts by identifying the similar doctor based on the patients' health requirements and cluster them using K-Means Efficient Clustering. Our proposed K-Means Clustering with Content Based Doctor Recommendation for Cancer (KMC-CBD) helps users to find an optimal solution. The core component of KMC-CBD Recommended system suggests patients with top recommended doctors similar to the other patients who already treated with that doctor and supports the choice of the doctor and the hospital for the patient requirements and their health condition. The recommendation System first computes K-Means Clustering is an unsupervised learning among Doctors according to their profile and list the Doctors according to their Medical profile. Then the Content based doctor recommendation System generates a Top rated list of doctors for the given patient profile by exploiting health data shared by the crowd internet community. Patients can find the most similar patients, so that they can analyze how they are treated for the similar diseases, and they can send and receive suggestions to solve their health issues. In order to the improve Recommendation system efficiency, the patient can express their health information by a natural-language sentence. The Recommendation system analyze and identifies the most relevant medical area for that specific case and uses this information for the recommendation task. Provided by users as well as the recommended system to suggest the right doctors for a specific health problem. Our proposed system is implemented in Python with necessary functions and dataset.