• Title/Summary/Keyword: Product recommendation service

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A Study on the Effects of After-purchase Feedback About Customer Service Quality on Purchase Process - Focusing on Internet Shopping Mall - (고객 서비스 품질에 대한 구매 후기 댓글이 구매과정에 미치는 영향 - 인터넷 쇼핑몰을 중심으로 -)

  • Shin, Chang-Nag;Kim, Young-Ei;Park, Young-Kyun
    • Journal of Distribution Research
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    • v.14 no.1
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    • pp.27-44
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    • 2009
  • This research classified the customer service factor of on-line shopping mall into tangibility, reliability, responsiveness, and empathy and analyzed the effect that the factors affect to consumer's purchase and re-purchase. If we present suggestions on the basis of these results of study, we would provide next two points: First, purchasers have utilized online shopping mall who pursued free from hard sell that being done in off-line and convenience of purchase affected more by reliability and responsiveness such as the fame of shopping mall that visit, reliability of security, and quick product search than the Customer of After-purchase Feedback influence for online purchasers decision factor out of consumer's purchase and re-purchase by on-line shopping mall customer service factor. Second, This study analyzed that online re-purchaser recognized the Customer of After-purchase Feedback factor high and built their loyalty through friendly emotion of on-line shopping mall and satisfaction of shopping mall service, and recommendation. In addition, they behave themselves as an affirmative messenger that is role of the Customer of After-purchase Feedback that make active opinion presentation and participation through community by important adjustment impact that empathy factor of on-line shopping mall customer service.

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An Exploratory Study on the Components of Visual Merchandising of Internet Shopping Mall (인터넷쇼핑몰의 VMD 구성요인에 대한 탐색적 연구)

  • Kim, Kwang-Seok;Shin, Jong-Kuk;Koo, Dong-Mo
    • Journal of Global Scholars of Marketing Science
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    • v.18 no.2
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    • pp.19-45
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    • 2008
  • This study is to empirically examine the primary dimensions of visual merchandising (VMD) of internet shopping mall, namely store design, merchandise, and merchandising cues, to be a attractive virtual store to the shoppers. The authors reviewed the literature related to the major components of VMD from the perspective of the AIDA model, which has been mainly applied to the offline store settings. The major purposes of the study are as follows; first, tries to derive the variables related with the components of visual merchandising through reviewing the existing literatures, establish the hypotheses, and test it empirically. Second, examines the relationships between the components of VMD and the attitude toward the VMD, however, putting more emphasis on finding out the component structure of the VMD. VMD needs to be examined with the perspective that an online shopping mall is a virtual self-service or clerkless store, which could reduce the number of employees, help the shoppers search, evaluate and purchase for themselves, and to be explored in terms of the in-store persuasion processes of customers. This study reviewed the literatures related to store design, merchandise, and merchandising cues which might be relevant to the store, product, and promotion respectively. VMD is a total communication tool, and AIDA model could explain the in-store consumer behavior of online shopping. Store design has to do with triggering a consumer attention to the online mall, merchandise with a product related interest, and merchandising cues with promotions such as recommendation and links that induce the desire to pruchase. These three steps might be seen as the processes for purchase actions. The theoretical rationale for the relationship between VMD and AIDA could be found in Tyagi(2005) that the three steps of consumer-oriented merchandising are a store, a product assortment, and placement, in Omar(1999) that three types of interior display are a architectural design display, commodity display, and point-of-sales(POS) display, and in Davies and Ward(2005) that the retail store interior image is related to an atmosphere, merchandise, and in-store promotion. Lee et al(2000) suggested as the web merchandising components a merchandising cues, a shopping metaphor which is an assistant tool for search, a store design, a layout(web design), and a product assortment. The store design which includes differentiation, simplicity and navigation is supposed to be related to the attention to the virtual store. Second, the merchandise dimensions comprising product assortments, visual information and product reputation have to do with the interest in the product offerings. Finally, the merchandising cues that refer to merchandiser(MD)'s recommendation of products and providing the hyperlinks to relevant goods for the shopper is concerned with attempt to induce the desire to purchase. The questionnaire survey was carried out to collect the data about the consumers who would shop at internet shopping malls frequently. To select the subject malls, the mall ranking data announced by a mall rating agency was used to differentiate the most popular and least popular five mall each. The subjects was instructed to answer the questions after navigating the designated mall for five minutes. The 300 questionnaire was distributed to the consumers, 166 samples were used in the final analysis. The empirical testing focused on identifying and confirming the dimensionality of VMD and its subdimensions using a structural equation modeling method. The confirmatory factor analysis for the endogeneous and exogeneous variables was carried out in four parts. The second-order factor analysis was done for a store design, a merchandise, and a merchandising cues, and first-order confirmatory factor analysis for the attitude toward the VMD. The model test results shows that the chi-square value of structural equation is 144.39(d.f 49), significant at 0.01 level which means the proposed model was rejected. But, judging from the ratio of chi-square value vs. degree of freedom, the ratio was 2.94 which smaller than an acceptable level of 3.0, RMR is 0.087 which is higher than a generally acceptable level of 0.08. GFI and AGFI is turned out to be 0.90 and 0.84 respectively. Both NFI and NNFI is 0.94, and CFI 0.95. The major test results are as follows; first, the second-order factor analysis and structural equational modeling reveals that the differentiation, simplicity and ease of identifying current status of the transaction are confirmed to be subdimensions of store design and to be a significant predictors of the dependent variable. This result implies that when designing an online shopping mall, it is necessary to differentiate visually from other malls to improve the effectiveness of the communications of store design. That is, the differentiated store design raise the contrast stimulus to sensory organs to promote the memory of the store and to have a favorable attitude toward the VMD of a store. The results that navigation which means the easiness of identifying current status of shopping affects the attitude to VMD could be interpreted that the navigating processes via the hyperlinks which is characteristics of an internet shopping is a complex and cognitive process and shoppers are likely to lack the sense of overall structure of the store. Consequently, shoppers are likely to be alost amid shopping not knowing where to go. The orientation tool enhance the accessibility of information to raise the perceptive power about the store environment.(Titus & Everett 1995) Second, the primary dimension of merchandise and its subdimensions was confirmed to be unidimensional respectively, have a construct validity, and nomological validity which the VMD dimensions supposed to have a positive correlation with the dependent variable. The subdimensions of product assortment, brand fame and information provision proved to have a positive effect on the attitude toward the VMD. It could be interpreted that the more plentiful the product and brand assortment of the mall is, the more likely the shoppers to favor it. Brand fame and information provision as well affect the VMD attitude, which means that the more famous the brand, the more likely the shoppers would trust and feel familiar with the mall, and the plentifully and visually presented information could have the shopper have a favorable attitude toward the store VMD. Third, it turned out to be that merchandising cue of product recommendation and hyperlinks affect the VMD attitude. This could be interpreted that recommended products could reduce the uncertainty related with the purchase decision, and the hyperlinks to relevant products would help the shopper save the cognitive effort exerted into the information search and gathering, which could lead to a favorable attitude to the VMD. This study tried to sheds some new light on the VMD of online store by reviewing the variables mentioned to be relevant with offline VMD in the existing literatures, and tried to link the VMD components from the perspective of AIDA model. The effect size of the VMD dimensions on the attitude was in the order of the merchandise, the store design and the merchandising cues.It is said that an internet has an unlimited place for display, however, the virtual store is not unlimited since the consumer has a limited amount of cognitive ability to process the external information and internal memory. Particularly, the shoppers are likely to face some difficulties in decision making on account of too many alternative and information overloads. Therefore, the internet shopping mall manager should take into consideration the cost of information search on the part of the consumer, to establish the optimal product placements and search routes. An efficient store composition would be possible by reducing the psychological burdens and cognitive efforts exerted to information search and alternatives evaluation. The store image is in most part determined by the product category and its brand it deals in. The results of this study support this proposition that the merchandise is most important to the VMD attitude than other components, the manager is required to take a strategic approach to VMD. The internet users are getting more accustomed and more knowledgeable about the internet media and more likely to accept the internet as a shopping channel as the period of time during which they use the internet to shop become longer. The web merchandiser should be aware that the product introduction using a moving pictures and a bulletin board become more important in order to present the interactive product information visually and communicate with customers more actively, therefore leading to making the quantity and quality of product information more rich.

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A Research on the Characteristics of Virtual Reality Stores -Focused on Hyundai VR Store and eBay VR Department Store- (가상현실 점포의 특성에 관한 연구 -현대백화점 VR 스토어와 eBay VR 백화점 사례를 중심으로-)

  • Jang, Ju Yeun;Chun, Jaehoon
    • Journal of the Korean Society of Clothing and Textiles
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    • v.42 no.4
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    • pp.671-688
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    • 2018
  • This study investigates the characteristics of VR stores that emerged as new fashion communication media. Two case studies on Hyundai and eBay VR Department stores were conducted along with a discussion of the function and meaning of the fashion VR store. The results showed that both stores provide novel shopping experiences; however, the two were differentiated in terms of production method and technology implementation level. Functional aspects such as providing shopping efficiency and purchasing service was insufficient in both stores. Instead, they were complementing by means of product rotation, recommendation system, voice guidance, or linkage with an online shopping mall. In experiential aspects, both stores provided a strong sense of immersion. Hyundai VR store enhanced immersion with a high resolution image of a real offline store; however, it lacked in the ability to provide multisensory stimulation such as kinetic sense or auditory stimulation. The eBay VR Department store intensified the immersion experience by providing auditory stimulation as well as visual stimulation that enhanced the speed and distance sense through the utilization of animation. However, the extent of experience was limited in terms of agency and transformation because of the low interactivity found in both store systems.

Configuration System through Vector Space Modeling In I-Commerce (전자상거래에서의 벡터 공간 모델링을 통한 Configuration 시스템)

  • 김세형;조근식
    • Journal of Intelligence and Information Systems
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    • v.7 no.1
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    • pp.149-159
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    • 2001
  • There have been lots of researches for providing a personalized service to a customer using one-to-one marketing and collaborative filtering techniques in E-Commerce. However, there are technical difficulties for providing the recommendation of products far users, which often involve high complexity of computation. In this paper, we have presented an integrated method of classification problem solving method and constraint based configuration techniques. This method can reduce a complexity of computation by classifying a solution domain space that has a higher complexity of composition. Thereafter, we have modeled customers constraints and the components of products to configure a complete system by passing it to constraint processing module in Constraint Satisfaction Problems. Constraint-based configuration uses the constraint propagation using the constraints of buyers and the constraints among PC components to configure a proper product for a customer. We have transformed and applied vector space modeling method in the field of information retrieval to consider a customer satisfaction in addition to the CSP. Finally, we have applied our system to test data fur evaluating a customers satisfaction and performance of the proposed system.

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Design and Implementation of Chronic Disease Risk Analysis System according to Personalized Food Intake Preferences (개인 식품섭취 선호도에 따른 만성질환 발생 위험도 분석 시스템 설계 및 구현)

  • Jeon, So Hye;Kim, Nam Hyun
    • Journal of the Institute of Electronics and Information Engineers
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    • v.51 no.3
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    • pp.147-155
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    • 2014
  • While variety of content on the internet has increased with the development of IT and person's needs about suitable information are increasing rapidly, studies for personalized service have been actively performed. In the study, we proposed the Hypertension and Diabetes risk analysis system according to personal food intake preference using the analysis method of buying preferences in product recommendation system. For the analysis of food intake preference, the Pearson correlation coefficient is used to calculate similarity weights between each reference analysis data and sample data and then reference data should be grouping into the similarity weights and calculating risk of hypertension and diabetes each group. To evaluate the significance of this system, 1,021 subjects are applied the system. Hypertension and diabetes groups' risk is significant higher than normal group statistically so, it is confirmed that food intake preference and the diseases were relevant. In this paper, we verify the validity of hypertension and diabetes risk analysis system using a personal food intake preference.

Clustering Method based on Genre Interest for Cold-Start Problem in Movie Recommendation (영화 추천 시스템의 초기 사용자 문제를 위한 장르 선호 기반의 클러스터링 기법)

  • You, Tithrottanak;Rosli, Ahmad Nurzid;Ha, Inay;Jo, Geun-Sik
    • Journal of Intelligence and Information Systems
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    • v.19 no.1
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    • pp.57-77
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    • 2013
  • Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.

Storm-Based Dynamic Tag Cloud for Real-Time SNS Data (실시간 SNS 데이터를 위한 Storm 기반 동적 태그 클라우드)

  • Son, Siwoon;Kim, Dasol;Lee, Sujeong;Gil, Myeong-Seon;Moon, Yang-Sae
    • KIPS Transactions on Software and Data Engineering
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    • v.6 no.6
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    • pp.309-314
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    • 2017
  • In general, there are many difficulties in collecting, storing, and analyzing SNS (social network service) data, since those data have big data characteristics, which occurs very fast with the mixture form of structured and unstructured data. In this paper, we propose a new data visualization framework that works on Apache Storm, and it can be useful for real-time and dynamic analysis of SNS data. Apache Storm is a representative big data software platform that processes and analyzes real-time streaming data in the distributed environment. Using Storm, in this paper we collect and aggregate the real-time Twitter data and dynamically visualize the aggregated results through the tag cloud. In addition to Storm-based collection and aggregation functionalities, we also design and implement a Web interface that a user gives his/her interesting keywords and confirms the visualization result of tag cloud related to the given keywords. We finally empirically show that this study makes users be able to intuitively figure out the change of the interested subject on SNS data and the visualized results be applied to many other services such as thematic trend analysis, product recommendation, and customer needs identification.

Intention to Participate Crowdfunding based on Trust and Perceived Risk: An Exploratory Study with Comparison between Korea and Austria (이용자의 신뢰와 위험인지에 따른 크라우드펀딩(Crowdfunding) 참여의도: 한국과 오스트리아 탐색적 비교 연구)

  • JiHyun Lee;SangAh Park;DongBack Seo
    • Information Systems Review
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    • v.22 no.1
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    • pp.125-146
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    • 2020
  • With the penetration of the Internet and e-commerce, a 'crowdfunding' has emerged as a new way of financing. Crowdfunding has the advantage for a person to able to a simple way to finance her/his an innovative product or service from crowd. However, the success rate for crowdfunding projects is less than half. In this study, we introduce social exchange theory to explore the impact of trust and perceived psychological risk on the intention to participate in a crowdfunding website. Different from previous studies that have focused on a crowdfunding creator, we consider two different perspectives of a project creator and a project supporter. In addition, we compare perceptions of crowdfunding in different cultural contexts by conducting survey in two different countries Korea and Austria. Result shows that trust in recommendation and trust in website have different impacts on the intention to participate from two different perspectives. It also shows that perception of the quality and transparency of information provided by crowdfunding website has greater impact on trust in Korea than that in Austria. In case of perception of psychological risk, it has a negative impact on Austria's intention to create or support a project. On the other hand, it has relatively small impact on the intention to support and does not affect the intention to create a project in Korea.

Business Application of Convolutional Neural Networks for Apparel Classification Using Runway Image (합성곱 신경망의 비지니스 응용: 런웨이 이미지를 사용한 의류 분류를 중심으로)

  • Seo, Yian;Shin, Kyung-shik
    • Journal of Intelligence and Information Systems
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    • v.24 no.3
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    • pp.1-19
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    • 2018
  • Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.