• Title, Summary, Keyword: Recommendation

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A Web Recommendation System using Grid based Support Vector Machines

  • Jun, Sung-Hae
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.7 no.2
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    • pp.91-95
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    • 2007
  • Main goal of web recommendation system is to study how user behavior on a website can be predicted by analyzing web log data which contain the visited web pages. Many researches of the web recommendation system have been studied. To construct web recommendation system, web mining is needed. Especially, web usage analysis of web mining is a tool for recommendation model. In this paper, we propose web recommendation system using grid based support vector machines for improvement of web recommendation system. To verify the performance of our system, we make experiments using the data set from our web server.

An Analysis of Customer Preferences of Recommendation Techniques and Influencing Factors: A Comparative Study of Electronic Goods and Apparel Products (추천기법별 고객 선호도 및 영향요인에 대한 분석: 전자제품과 의류군에 대한 비교연구)

  • Park, Yoon-Joo
    • Information Systems Review
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    • v.18 no.2
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    • pp.59-77
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    • 2016
  • Although various recommendation techniques have been applied to the e-commerce market, few studies compare the intent to use these techniques from the customer's perspective. In this paper, we conduct a comparative analysis of customers' intention to use five recommendation techniques widely adapted by online shopping malls and focus on the differences in purchasing electronic goods and apparel products. The recommendation techniques are as follows: best-seller recommendation, merchandiser recommendation, content-based recommendation, collaborative filtering recommendation, and social recommendation. Additionally, we examine which factors influence customer intent to use the recommendation services. Data were collected through a survey administered to 220 e-commerce users with prior experience with recommendation services. Collected data were examined using analysis of variance and regression analysis. Results indicate statistically significant differences in customers' intention to use recommendation services according to the recommendation technique. In particular, the best-seller recommendation technique is preferred when purchasing electronic goods, whereas the content-based recommendation technique is preferred for apparel purchases. Factors such as personal characteristics and personality, purchasing tendency, as well as perception of the product or recommendation service affect a customer's intention to use a recommendation service. However, the influence of these factors varies depending on the recommendation technique. This study provides guidelines for companies to adopt appropriate recommendation techniques according to product categories and personal characteristics of customers.

An Intelligent Recommendation Service System for Offering Halal Food (IRSH) Based on Dynamic Profiles

  • Lee, Hyun-ho;Lee, Won-jin;Lee, Jae-dong
    • Journal of Korea Multimedia Society
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    • v.22 no.2
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    • pp.260-270
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    • 2019
  • As the growth of developing Islamic countries, Muslims are into the world. The most important thing for Muslims to purchase food, ingredient, cosmetics and other products are whether they were certified as 'Halal'. With the increasing number of Muslim tourists and residents in Korea, Halal restaurants and markets are on the rise. However, the service that provides information on Halal restaurants and markets in Korea is very limited. Especially, the application of recommendation system technology is effective to provide Halal restaurant information to users efficiently. The profiling of Halal restaurant information should be preceded by design of recommendation system, and design of recommendation algorithm is most important part in designing recommendation system. In this paper, an Intelligent Recommendation Service system for offering Halal food (IRSH) based on dynamic profiles was proposed. The proposed system recommend a customized Halal restaurant, and proposed recommendation algorithm uses hybrid filtering which is combined by content-based filtering, collaborative filtering and location-based filtering. The proposed algorithm combines several filtering techniques in order to improve the accuracy of recommendation by complementing the various problems of each filtering. The experiment of performance evaluation for comparing with existed restaurant recommendation system was proceeded, and result that proposed IRSH increase recommendation accuracy using Halal contents was deducted.

The Effects of Social Information on Recommendation Performance According to the Product Involvement Level (제품관여 수준에 따라 소셜 정보가 추천 성능에 미치는 영향)

  • Song, Hee Seok;Joo, Seok Jeong;Lee, Jae Hoon
    • Journal of Information Technology Applications and Management
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    • v.21 no.4_spc
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    • pp.361-379
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    • 2014
  • With the rapid increase of social network usage, there are emerging trends of adopting social information among online users in building recommendation system. This study aims to investigate whether the additional usage of social information can improve recommendation performance in recommendation system and how much the improvement can be different according to the product involvement level. As an experiment result, social information does not affect positively to the recommendation accuracy but affect significantly to the recommendation quality. Also social information contributed more sensitively to the improvement of recommendation quality in high product involvement domain.

Performance Analysis of Group Recommendation Systems in TV Domains

  • Kim, Noo-Ri;Lee, Jee-Hyong
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.15 no.1
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    • pp.45-52
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    • 2015
  • Although researchers have proposed various recommendation systems, most recommendation approaches are for single users and there are only a small number of recommendation approaches for groups. However, TV programs or movies are most often viewed by groups rather than by single users. Most recommendation approaches for groups assume that single users' profiles are known and that group profiles consist of the single users' profiles. However, because it is difficult to obtain group profiles, researchers have only used synthetic or limited datasets. In this paper, we report on various group recommendation approaches to a real large-scale dataset in a TV domain, and evaluate the various group recommendation approaches. In addition, we provide some guidelines for group recommendation systems, focusing on home group users in a TV domain.

A Study on the effect of product recommendation system on customer satisfaction: focused on the online shopping mall

  • CHO, Ba-Da;POTLURI, Rajasekhara Mouly;YOUN, Myoung-Kil
    • The Journal of Industrial Distribution & Business
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    • v.11 no.2
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    • pp.17-23
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    • 2020
  • Purpose: The purpose of this study is to understand the effect of the unique product recommendation system on customer satisfaction. Research design, data and methodology: The survey method used the self-recording way in which the respondents selected for the study and distributed 300 questionnaires, and with due personal care, researchers collected all the distributed questionnaires. Results: The result implies that the characteristics of the product recommendation system should be more secure and developed. Conclusions: The aspects of the product recommendation system were selected as factors of price fairness, accuracy, and quality through previous studies, and the empirical analysis of the effect of the characteristics of the product recommendation system on customer satisfaction was summarized as follows. Among the attributes of the product recommendation system, the attributes of price fairness, accuracy, and quality affect customer satisfaction. Among them, the beta value of quality was the highest, and the effect of quality was the largest among the three factors. Based on the results of the study, the implications for the characteristics of the product recommendation system are summarized as follows. The aspects of the product recommendation system have a positive effect on customer satisfaction, so it is necessary to fill the needs of consumers based on the survey focused on quality

A Large Number of Consumer Recommendations? or A Small Number of Friend Recommendations? : Purchasing Decision Making based on SNS (다수의 대중추천인가? 소수의 지인추천인가? : 소셜 네트워크 기반의 구매의사결정)

  • Shim, Seon-Young
    • The Journal of Society for e-Business Studies
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    • v.17 no.3
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    • pp.15-41
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    • 2012
  • Recently, there happens many purchasing cases encouraged by friends' recommendation in SNS (Social Network Service). This study investigates the effect of friend recommendation on consumers' purchasing heuristic. For this purpose, we compare the effect of friend recommendation with consumer recommendation in terms of trustworthy, specialty, relevancy. Usually, the frequency of friend recommendation is far lower than that of consumer recommendation. Hence, we examine how the effect of information source (friend recommendation or consumer recommendation) is moderated by the frequency of recommendation, as well. As results, this study finds out that, under the same frequency, friend recommendation does not have significantly stronger effect on the purchasing heuristic, although friend recommendation is evidenced as one of significant heuristic inducers. However, in terms of trustworthy, friend recommendation is significantly superior to the consumer recommendation. Moreover, under sufficiently higher frequency, friend recommendation works as better heuristic factor than consumer recommendation. The results deliver managerial implications in the perspective of understanding consumers' purchasing decisions and responding strategies of firms.

A Personalized Recommendation Procedure for E-Commerce

  • Kim, Jae-Kyeong;Cho, Yoon-Ho;Kim, Woo-Ju;Kim, Je-Ran;Suh, Ji-Hae
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • pp.192-197
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    • 2001
  • A recommendation system tracks past actions of a group of users to make a recommendation to individual members of the group. The computer-mediated marketing and commerce have grown rapidly nowadays so the concerns about various recommendation procedures are increasing. We introduce a recommendation methodology by which e-commerce sites suggest new products of services to their customers. The suggested methodology is based on web log analysis, product taxonomy, and association rule mining. A product recommendation system is developed based on our suggested methodology and applied to a Korean internet shopping mall. The validity of our recommendation system is discussed with the analysis of a real internet shopping mall case.

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Effects of Recommendation Selling in Family Restaurants on Customer Attitudes, Customer Satisfaction, Customer Purchase Decision Making (패밀리 레스토랑의 메뉴 권유 판매가 고객 태도, 만족, 구매 의사 결정에 미치는 영향)

  • Lee, Yeon-Jung;Ju, Hyun-Sik
    • Culinary science and hospitality research
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    • v.12 no.2
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    • pp.73-87
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    • 2006
  • The purpose of this study is to investigate if recommendation selling (methods of recommendation selling, a key word used for recommendation, and employee attitude) influences the customers' menu decision. The results of the study are as follows: 'Menu picture' and 'explanation by word' among the tools used by employees for recommendation were found to influence customers' menu decision. The words such as 'new menu' and 'special only today' used by employees for recommendation were found to influence customers' menu decision. Employees' attitude elements such as 'interesting explanation', 'dressed up tidy', 'strong intention', and 'patience' were found to influence customer's menu decision. 'Recommendation selling' in the food and beverage industry means 'employees help customers make a good decision on food and beverage service'. This study makes an important contribution to the food industry in terms of providing substantial marketing strategies.

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The Effect of Online Travel Agency's Recommendation Information on Purchase Decision Making and Reuse Intention (온라인 여행사의 추천정보가 구매의사결정과 재사용의도에 미치는 영향)

  • Chung, Nam-Ho;Um, Tae-Hyee
    • 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.