• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• Journal of International Academy of Physical Therapy Research
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• v.3 no.2
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• pp.469-474
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• 2012

The purpose of this study is to analyze the correlation between the stature and the muscle performance ratings and the subjective discomfort rations at performing lower arm's pronation and supination according to change sin the height of working table for more efficient performance at designing a working table and performing a work. For the purpose, this study conducted an experiment targeting 40 people in their 20s, who were classified into 4 groups each group composing 10 people at intervals of 5cm from the standard stature of 166.5cm. The experiment measured the maximum isometric pronation and the supination muscular power, and at measuring the factors, the heights of working tables were set as 800mm, 850mm, and 900mm. From the measurement results, it was found that the stature and the maximum muscular power was correlated. That is, as the experiment groups's average stature is higher, the maximum muscular power was higher. For the correlation between the motion patterns(pronation and supination) and the maximum muscular power, it was seen that the maximum muscular power was higher at performing the pronation than the supination. In the correlation between motion patterns and the subjective discomfort ratings, it was seen that the subjective discomfort rating was higher at performing the supination than the pronation. For the correlation between height adjustment and the subjective discomfort ratings, as the height of working table was lower, the subject discomfort rating was lower. Therefore there was no difference in the maximum muscular power according to the height changes of working table, but it was found that as the working table was higher, the user felt more comfortable.

• Information Systems Review
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• v.23 no.1
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• pp.155-172
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• 2021

With the recent increase in online product purchases, a recommender system that recommends products considering users' preferences has still been studied. The recommender system provides personalized product recommendation services to users. Collaborative Filtering (CF) using user ratings on products is one of the most widely used recommendation algorithms. During CF, the item-based method identifies the user's product by using ratings left on the product purchased by the user and obtains the similarity between the purchased product and the unpurchased product. CF takes a lot of time to calculate the similarity between products. In particular, it takes more time when using text-based big data such as review data of Amazon store. This paper suggests a hybrid recommendation system using a 2-phase methodology and text data mining to calculate the similarity between products easily and quickly. To this end, we collected about 980,000 online consumer ratings and review data from the online commerce store, Amazon Kinder Store. As a result of several experiments, it was confirmed that the suggested hybrid recommendation system reflecting the user's rating and review data has resulted in similar recommendation time, but higher accuracy compared to the CF-based benchmark recommender systems. Therefore, the suggested system is expected to increase the user's satisfaction and increase its sales.

• Journal of applied mathematics & informatics
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• v.14 no.1_2
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• pp.353-363
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• 2004

The purpose of this paper is to present a model to design the layout of the user interface components that handles many numbers of qualitative factors. An alternate rating system is also proposed for the closeness relationship ratings between the various pairs of components evaluated by using GOMS (goals, operators, methods and selection rules) technique. The proposed model is applied to the design of the part of the user interface in order to obtain the best layout of the components. The results of the proposed model are compared with that of an existing model, which handles single qualitative factor applied to obtain the layouts of user interface components.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.10
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• pp.1965-1972
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• 1994

A standard on talker echo for ISDN telophone, mainly consisting of those on sending and receiving loudness ratings and terminal coupling loss(TCL), is necessary. Accordingly, if sending and receiving loudness ratings are pre-determined, we need a standard on TCL providing echo-free telephone communications to telephone users, and the standard can be classified into that weighted TCL($TCL_w$) and that on stability loss. In this paper, in order to make a national standard on TCL, based on users' perceived quality on a talker echo, we conducted user opinion tests on talker echo. From the results of correlation between echo and user opinion on quality and measurement on TCL of telephones, we proposed the standard as follows; we must preserve TCL_w of at least 40dB and stability loss of at least 10dB when overall loudness rating for ISDN telephone, sum of sending and receiving loudness ratings, is normalized to 10dB.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.953-964
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• 2006

Due to the usefulness of the collaborative filtering, it has been widely used in both the research and commercial field. However, there are still some challenges for it to be more efficient, especially the scalability problem, the sparsity problem and the cold start problem. In this paper. we address these problems and provide a novel distributed approach based on agents collaboration for the problems. We have tried to solve the scalability problem by making each agent save its users ratings and broadcast them to the users friends so that only friends ratings and his own ratings are kept in an agents local database. To reduce quality degradation of recommendation caused by the lack of rating data, we introduce a method using friends opinions instead of real rating data when they are not available. We also suggest a collaborative filtering algorithm based on user profile to provide new users with recommendation service. Experiments show that our suggested approach is helpful to the new user problem as well as is more scalable than traditional centralized CF filtering systems and alleviate the sparsity problem.

• The Journal of Korean Association of Computer Education
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• v.14 no.4
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• pp.63-70
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• 2011

Recommender systems, which predict and recommend items that may possibly draw users' interests, have been applied in various fields as e-commerce systems are widespread. Collaborative filtering, one of the major methodologies of recommender systems, recommends either items similar to those preferred by the user, or items preferred by the other similar user. Therefore, two problems determine its performance; one is correct estimation of similarity and the other is predicting the real rating of the recommended item. This study addresses the latter problem. Previous studies predict the real rating based on the mean of the ratings, but this study proposes a prediction based on the range of the ratings and investigates its performance through experiments. As a result, it is demonstrated that the proposed method improves the mean absolute error significantly, compared to the previous method.

• The Journal of Information Systems
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• v.27 no.1
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• pp.89-110
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• 2018

Purpose There is much information in customer reviews, but finding key information in many texts is not easy. Business decision makers need a model to solve this problem. In this study we propose a multi-topic sentiment analysis approach using Latent Dirichlet Allocation (LDA) for user-generated contents (UGC). Design/methodology/approach In this paper, we collected a total of 104,039 hotel reviews in seven of the world's top tourist destinations from TripAdvisor (www.tripadvisor.com) and extracted 30 topics related to the hotel from all customer reviews using the LDA model. Six major dimensions (value, cleanliness, rooms, service, location, and sleep quality) were selected from the 30 extracted topics. To analyze data, we employed R language. Findings This study contributes to propose a lexicon-based sentiment analysis approach for the keywords-embedded sentences related to the six dimensions within a review. The performance of the proposed model was evaluated by comparing the sentiment analysis results of each topic with the real attribute ratings provided by the platform. The results show its outperformance, with a high ratio of accuracy and recall. Through our proposed model, it is expected to analyze the customers' sentiments over different topics for those reviews with an absence of the detailed attribute ratings.

• 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.

• Journal of Korean Society for Quality Management
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• v.40 no.4
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• pp.559-576
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• 2012

Purpose: This research aims at quality improvement of website services on the internet. Methods: SERVQUAL is redefined for web environments as sixteen statements in five dimensions to identify user requirements for websites and measure their importance ratings. Quality Function Deployment (QFD) is used to integrate user requirements into service development for websites so that management and design guidelines can be obtained for user-oriented websites. Fuzzy set theory is introduced to resolve the ambiguity and subjectivity of user requirements in the House of Quality (HOQ). Results: The priorities of design characteristics are extracted from the Fuzzy QFD for University websites. Conclusion: It is expected to provide quality services in less time with less effort, if the results of the Fuzzy QFD are used to provide services in a strategic way under limited resources.