• Information Systems Review
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• v.17 no.3
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• pp.95-111
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• 2015

The recommendation system automatically provides the predicted items which are expected to be purchased by analyzing the previous customer behaviors. This recommendation system has been applied to many e-commerce businesses, and it is generating positive effects on user convenience as well as the company's revenue. However, there are several limitations of the existing recommendation systems. They do not reflect specific criteria for evaluating products or the factors that affect customer buying decisions. Thus, our research proposes a collaborative recommendation model algorithm that utilizes each customer's online product reviews. This study deploys topic modeling method for customer opinion mining. Also, it adopts a kernel-based machine learning concept by selecting kernels explaining individual similarities in accordance with customers' purchase history and online reviews. Our study further applies a multiple kernel learning algorithm to integrate the kernelsinto a combined model for predicting the product ratings, and it verifies its validity with a data set (including purchased item, product rating, and online review) of BestBuy, an online consumer electronics store. This study theoretically implicates by suggesting a new method for the online recommendation system, i.e., a collaborative recommendation method using topic modeling and kernel-based learning.

• IE interfaces
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• v.15 no.2
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• pp.107-113
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• 2002

Recent reports studied that injuries or deaths frequently occurred in consumer product accidents by product defects. Broadly speaking, product liability is liability which is imposed upon a manufacturer or other seller for personal injury, death, property damage and/or commercial loss arising with respect to a product or service provided by it. In this study, we want to search a method of prevention against appling PL laws. The way was researching on the level of appreciation of PL law, warning messages's means and design criteria for seller or consumer of child toys. As a result, most people didn't understand PL laws. Although they read them before purchasing child toy, many consumers didn't differentiate means of "Notice", "Warning", and "Danger" in warning messages. In addition, they considered important factors in warning messages as notice warning, safety mark(UL, etc), age recommendation and color in order. This study will be effective to search a method of prevention against PL laws.

• Journal of Dairy Science and Biotechnology
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• v.19 no.1
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• pp.30-38
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• 2001

The most important task in Korean dairy industry is to keep the seasonal and annual balance of raw milk supply and demand. Too much surplus milk supply which causes dumping sale of market milk makes dairy industries get in trouble of management, and eventually affects to farmers and consumers economically. As balancing of supply and demand is so important in the fee economic market system, the adaption of the quota system of milk production and seasonal price differentiation has been recommended very often as a method of controlling the milk supply and demand. However, this recommendation did not go through successfully due to the strong objection of dairy farmers. Recently, the voice of consumer's requirement for safer and more hygienic, and high protein, low fat level dairy product is getting stronger. By knowledge of this kind changes, quality improvement in nutrients and hygiene is the most positive way to expand the volume of milk consumption. To meet the consumer's demand, therefore, it is necessary to revise the level of milk fat content and the hygienic grading system for the payment system of raw milk.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.2
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• pp.221-228
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• 2020

This study suggests an alternative to the conventional collaborative filtering method for predicting consumer choice, using case-based reasoning. The algorithm of case-based reasoning determines the similarity between the alternative sets that each subject chooses. Case-based reasoning uses the inverse of the normalized Euclidian distance as a similarity measurement. This normalized distance is calculated by the ratio of difference between each attribute level relative to the maximum range between the lowest and highest level. The alternative case-based reasoning based on similarity predicts a target subject's choice by applying the utility values of the subjects most similar to the target subject to calculate the utility of the profiles that the target subject chooses. This approach assumes that subjects who deliberate in a similar alternative set may have similar preferences for each attribute level in decision making. The result shows the similarity between comparable alternatives the consumers consider buying is a significant factor to predict the consumer choice. Also the interaction effect has a positive influence on the predictive accuracy. This implies the consumers who looked into the same alternatives can probably pick up the same product at the end. The suggested alternative requires fewer predictors than conjoint analysis for predicting customer choices.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.211-221
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• 2024

Live streaming commerce has emerged as an innovative e-commerce model. This study, based on the Elaboration Likelihood Model (ELM), aims to explore the impact of Key Opinion Consumers' (KOCs) attributes in live streaming commerce on purchase intentions on short video platforms. A survey was conducted with 411 consumers, and data analysis and hypothesis testing were performed using SPSS 24.0 and AMOS 23.0 software. Research has found that differences in consumers' information processing abilities lead to different pathway selections. Central route factors such as recommendation consistency, product involvement, and professionalism, as well as peripheral route factors such as recommendation timeliness, all have significant positive effects on consumers' purchase intention. However, visual cues in the peripheral route do not have a significant impact. This study aims to provide theoretical support and practical guidance for the development of the live streaming commerce industry, and to help companies adjust their promotion strategies based on differences in consumer information processing, thereby improving purchase conversion rates.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.495-503
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• 2015

This study attempts to reveal that the connection between consumers' self enhancement and consumer-brand relationship can be formed from brand experiences not only through a direct use of products or brands but also through companies' marketing activities including advertising or promotion, in that consumers' sensual, affective, cognitive, and behavioral response to brands are included in brand experiences. After experiencing the Samsung Galaxy S brand with manipulated stimulus, 248 respondents' positive emotion and self-enhancement were increased. Also the positive emotion induced by brand experience increase self-enhancement. This study also shows that brand experience and self-enhancement can significantly affect consumer-brand relationship which in turn affects brand loyalty such as satisfaction, recommendation intention, and repurchase intention. Experiencing strong, favorable, and attractive brand personalities may help consumers to increase positive emotions with self-enhancement and help companies to have brand loyalty through consumer-brand relationship.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.137-148
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• 2020

While the diversity of consumer choices due to the increased information in the digital age is positive, there are also many problems with the information overload. There are even situations in which consumers can not make the best choices under the weight of information. The purpose of this study is to look at how information overload plays a role in influencing online consumer behavior. With factors related to characteristics of the shopping mall, the recognition of the mall, the quality of the mall, the composition of the shopping mall, and the purchase recommendation service were set to analyze how these variables change the behavior of online consumers when information overload appears. According to the analysis results, all of characteristic factors of shopping malls set up in this paper are analyzed to have a constant effect on the behavior of online consumers, and information overload also has a constant medium effect on the recognition of shopping malls, the quality and the structure of shopping malls, and the provision of purchase recommendation services. And characteristic factors of shopping malls are also showing positive effects on online consumer behavior in information overload situations.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.81-93
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• 2021

This study examined the effects of cosmetic selection attributes on consumers' purchase propensity and recommend intention. In addition, the moderating effect according to the consumer's deal proneness was verified. Finally, a multi-group analysis was conducted to verify the difference in the research model path according to the information source. Through this study, the selection attributes of consumers who purchase cosmetics were clarified. This study aims to meet the needs and demands of the related industry for more detailed and effective strategic insights by clarifying the structure of the influence of these selection attributes on purchase intention and recommendation intention according to the discount purchase intention. In order to achieve the research objectives due to this necessity, a questionnaire of 258 Korean female consumers was collected and used for research. The analysis results showed that product selection attributes, purchase intention, and recommendation intention all had a positive influence. As a result of analyzing the moderation effect according to the consumer's Deal Proneness, the results showed a moderating effect between the selection attribute and the purchase propensity, the selection attribute and the recommend intention, and the purchase propensity and the recommend intention. Finally, it was partially adopted as a result of conducting a multi-group analysis to verify whether individual paths of the model differ according to information sources.