• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.49-56
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• 2024

There have been emerging many use-cases applying recommendation systems especially in online platform. Although the performance of recommendation systems is affected by a variety of factors, selecting appropriate features is difficult since most of recommendation systems have sparse data. Conventional matrix factorization (MF) method is a basic way to handle with problems in the recommendation systems. However, the MF based scheme cannot reflect non-linearity characteristics well. As deep learning technology has been attracted widely, a deep neural network (DNN) framework based collaborative filtering (CF) was introduced to complement the non-linearity issue. However, there is still a problem related to feature embedding for use as input to the DNN. In this paper, we propose an effective method using singular value decomposition (SVD) based feature embedding for improving the DNN performance of recommendation algorithms. We evaluate the performance of recommendation systems using MovieLens dataset and show the proposed scheme outperforms the existing methods. Moreover, we analyze the performance according to the number of latent features in the proposed algorithm. We expect that the proposed scheme can be applied to the generalized recommendation systems.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.752-770
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• 2019

The purpose of this study is to incorporate telemarketing processes to improve telemarketing performance. For this application, we have attempted to mix the model of machine learning to extract potential customers with personalisation techniques to derive recommended products from actual contact. Most of traditional recommendation systems were mainly in ways such as collaborative filtering, which predicts items with a high likelihood of future purchase, based on existing purchase transactions or preferences for products. But, under these systems, new users or items added to the system do not have sufficient information, and generally cause problems such as a cold start that can not obtain satisfactory recommendation items. Also, indiscriminate telemarketing attempts can backfire as they increase the dissatisfaction and fatigue of customers who do not want to be contacted. To this purpose, this study presented a multi-purpose hybrid recommendation algorithm to achieve two goals: to select customers with high possibility of contact, and to recommend products to selected customers. In addition, we used subscription data from telemarketing agency that handles insurance products to derive realistic applicability of the proposed recommendation system. Our proposed recommendation system would certainly solve the cold start and scarcity problem of existing recommendation algorithm by using contents information such as customer master information and telemarketing history. Also. the model could show excellent performance not only in terms of overall performance but also in terms of the recommendation success rate of the unpopular product.

• 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.

• Journal of Korean Society for Quality Management
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• v.49 no.1
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• pp.17-29
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• 2021

Purpose: During 2015-2019, the average amount of political donation to the national assembly members in Korea was 1,000 won per person. Despite its benefits such as receiving tax credits, the donation system has not been actively practiced. This paper aims to promote political donations by suggesting a recommendation system of national assembly members by analysing the bills they proposed. Methods: In this paper, we propose a recommendation system based on two aspects: how similar the newly proposed or ammended bills are to the sponsors' interest (similarity index) and how much effort national assembly members put into those bills (intensity index). More than 25,000 bills were used to measure the recommendation quality index consisted with both the similarity and the intensity indices. Word2vec was used to calculate the similarity index of the bills proposed by the national assembly member to the sponsor's interest. The intensity index is calculated by diving the number of newly proposed or entirely revised bills with the number of senators who took part in those bills. Subsequently, we multiply the similarity index by the intensity index to obtain the recommendation quality index that can assist sponsors to identify potential assembly members for their donation. Results: We apply the proposed recommendation system to personas for illustration. The recommendation system showed an average f1 score about 0.69. The analysis results provide insights in recommendation for donation. Conclusion: n this study, the recommendation system was proposed to promote a political donation for national assembly members by creating the recommendation quality index based on the similarity and the intensity indices. We expect that the system presented in this paper will lower user barriers to political information, thereby boosting political sponsorship and increasing political participation.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.164-175
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• 2022

The OTT (Over The Top) platform promotes itself as a distinctive competitive advantage in that it allows users to stay on the platform longer and visit more often through a Personalized Recommendation Service. In this study, the characteristics of the Personalized Recommendation Service are divided into three categories: recommendation accuracy, recommendation diversity, and recommendation novelty. Then proposed a research model which affects the usefulness of users to recognize recommendation services by each characteristics and leads to satisfaction of expectations. The result of conducting an online survey of 300 people in their 20s and 30s who subscribe Netflix shows that the perceived usefulness increased when the accuracy, variety, and novelty of Netflix's Recommendation Service were high. It was also confirmed that high perceived usefulness leads to satisfaction of expectations before and after Netflix use. The derived research results can confirm the importance of evaluating the personalized recommendation service in terms of user experience and provide implications for ways to improve the quality of recommendation services.

• Journal of Korea Trade
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• v.27 no.1
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• pp.42-59
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• 2023

Purpose - As a leading source of foreign exchange and investment, tourism has grown in importance as a component of international trade. Accordingly, in recent decades much attention has been directed toward attracting foreign tourists and, in turn, positively affecting the recommendation intentions of foreign tourists. Despite such interests, there remains a dearth of empirical research on this issue. Moreover, prior research has focused primarily on the simple main effect of a certain factor on recommendation intentions. Therefore, the present study aims to (1) investigate the effect of overall satisfaction on the recommendation intentions of foreign tourists, and (2) examine the potential moderating effects of personal factors (i.e., age and destination image) on the association between overall satisfaction and recommendation intention. Design/methodology - Using a moderated moderation analysis of the data drawn from the 2018 International Visitor Survey conducted by the Korea Tourism Organization, this study proposes the three-way interaction effects of overall satisfaction, age, and destination image on recommendation intention. Findings - The findings of the study indicate that overall satisfaction is positively associated with recommendation intention and this relationship becomes stronger among younger tourists. The findings further indicate that the moderating effect of age on the relationship between overall satisfaction and recommendation intention depends on changes in the image of the destination. Specifically, the destination image exerts a positive moderating impact on the influence of age that moderates the overall satisfaction and recommendation intention relationship. Originality/value - Considering that the tourism economy has been severely affected by the current COVID-19 pandemic, this study contributes to a more accurate understanding of the factors affecting the recommendation intention, especially in times of crisis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.1
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• pp.38-46
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• 2004

To be successful in increasingly competitive Internet marketplace, it is essential to capture customer loyalty. This paper deals with an intelligent agent approach to incorporate customer's sensibility into an one-to-one recommendation service in on-line shopping mall. In this paper the focus of interest is on-line recommendation service algorithm for development of Human Sensibility based web agent system. The recommendation agent system composed of seven services including specialized algorithm. The on-line recommendation service algorithm use human sensibility ergonomics and on-line preference matching technologies to tailor to the customer the suggestion of goods and the description of store catalog. Customizing the system's behavior requires the parallel execution of several tasks during the interaction (e.g., identifying the customer's emotional preference and dynamically generating the pages of the store catalog). Most of the present shopping malls go through the catalog of goods, but the future shopping malls will have the form of intelligent shopping malls by applying the on-line recommendation service algorithm.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.95-100
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• 2009

The Semantic Web is an evolving extension of the WWW in which the semantics of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. The sementic web relied on the ontologies that structure underling data for the purpose of comprehensive and transportable machine understanding. The Semantic Web relies on the ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. And recommendation systems have been developed as a solution to the abundance of choice people face in many situations. This paper shows that the new recommendation method is suitable for effective recommendation on the semantic web. We present a new procedure for improving the effective recommendation by using the levelized context ontology. Our experimental results also confirm that our method has good recommendation time. Our proposed method can be generalized to fit other application domains.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2310-2332
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• 2020

In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.

• Journal of Information Technology Applications and Management
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• v.26 no.6
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• pp.89-101
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• 2019

Many researchers have been focused on designing beauty product recommendation system for a long time because of increased need of customers for personalized and customized recommendation in beauty product domain. In addition, as the application of the deep neural network technique becomes active recently, various collaborative filtering techniques based on the deep neural network have been introduced. In this context, this study proposes a deep neural network model suitable for beauty product recommendation by applying Neural Collaborative Filtering and Generalized Matrix Factorization (NCF + GMF) to beauty product recommendation. This study also provides an implementation of web API system to commercialize the proposed recommendation model. The overall performance of the NCF + GMF model was the best when the beauty product recommendation problem was defined as the estimation rating score problem and the binary classification problem. The NCF + GMF model showed also high performance in the top N recommendation.