A personalized recommendation procedure for Internet shopping support

Kim, Jae Kyeong; Cho, Yoon Ho; Kim, Woo Ju; Kim, Je Ran; Suh, Jihae

doi:10.1016/s1567-4223(02)00022-4

Cited by 101 publications

(41 citation statements)

References 13 publications

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“…Personalization aims to tailor services to individual needs, and its immediate objectives are to understand and to deliver highly focused, relevant content, services and products matched to users' needs and contexts (Adomavicius & Tuzhilin, 2005;Brusilovsky, 2002;Ho, 2006;Kim et al, 2009;Kim et al, 2002;Ricci & Werthner, 2006;). In eservices personalization and web adaptation have been employed in many different ways: (i) the personalization service can be designed and used as an advice-giving system to provide recommendations to each individual and to generate up-sell and cross-sell opportunities (ii) personalization services are used to (dynamically) structure the index of information, product pages based on click-stream analysis to minimize the users' search efforts, where personalized content based on the user's profile is generated.…”

Section: E-learning Recommender Systemsmentioning

confidence: 99%

Development of an e-Learning Recommender System Using Discrete Choice Models and Bayesian Theory: A Pilot Case in the Shipping Industry

Polydoropoulou¹,

Lambrou²

2012

Security Enhanced Applications for Information Systems

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Section: E-learning Recommender Systemsmentioning

confidence: 99%

Development of an e-Learning Recommender System Using Discrete Choice Models and Bayesian Theory: A Pilot Case in the Shipping Industry

Polydoropoulou¹,

Lambrou²

2012

Security Enhanced Applications for Information Systems

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“…To help consumers easily express their judgments, and domain experts easily evaluate product features, the linguistic terms are used to linguistically evaluate the importance of customer needs and ratings of product features. Seven linguistic sets, (1) Very Low (0,1,2), (2) Low (1,2,3), (3) Medium Low (2,3,4), (4) Medium (3,4,5), (5) Medium High (4,5,6), (6) High (5,6,7), (7) Very High (6,7,8), are allowable to describe the variables with one's subjective judgment.…”

Section: Linguistic Definition and Fuzzy Numbersmentioning

confidence: 99%

Applying Fuzzy Logic to Recommend Consumer Electronics

Cao

Liao

2005

Distributed Computing and Internet Technology

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Abstract. Depending on the type of the product, different kinds of personalized recommender systems can be built to guide the consumers in a large product feature space. In the approach, we present a fuzzy-based recommender system for those products that a general consumer does not buy very often, especially for consumer electronic products. For those consumer electronic products, it is difficult and not necessary to reason a customer's previous preferences because there may not be enough information about the customer's past purchases and the customer may have his specific requirements in each single purchase. Hence the system has specific domain knowledge and capability to interact with the consumer. Experimental results show the promise of our systems.

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“…Recommender systems and procedures that use them must be motivated in the context of customer behavior as described in (Kim, Cho, Kim, Kim, & Suh, 2002). In addition to cross-and up-selling behaviors, similar approaches to preference modeling are use to customize electronic catalogs (Yen & Kong, 2002).…”

Section: Conceptual Frameworkmentioning

confidence: 99%

Teaching e-Commerce Personalization Technology: The Need for a Comprehensive View

Sicilia

2005

JITE:Research

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Executive SummaryPersonalization technology has become an important topic in e-commerce, fostered by the emergence of the relationship-marketing paradigm. But teaching e-commerce personalization technology is a challenging task, since it requires a balance between marketing and management and technical implementation issues that must be calibrated according to the student's background, all of this without losing a holistic perspective of the subject. In this paper, we describe the result of our experiences in teaching personalization, sketching both a conceptual framework and a set of curricular objectives that provide a balanced and comprehensive view of the field in its current state.The conceptual framework for the topic area puts in relationship technical and business issues as part of marketing in e-business strategy. The main categories are the topics related to "partner interaction", including adaptive hypermedia and filtering of information, and "relationship maintenance", including user profiling and modeling along with common market segmentation techniques as related to the paradigm of relationship marketing. Concepts of human-computer interaction are required to aid in understanding the assessment techniques of personalization techniques, and privacy and legal issues should also be considered. The concept of CRM (Customer Relationship Management) systems crosscuts interaction and relationship maintenance issues, and could be used as a criteria for curriculum organization in some cases. Seven general curricular goals are identified as key aspects of the curriculum in personalization technology, including foundations of relationship marketing, user profiling and modeling, hypermedia and adaptive hypermedia concepts, the use of personalization as a tool in marketing strategy, CRM, privacy and legal issues and mass customization. The four first aspects are considered as the core of the curriculum, and they should be addressed to some extent depending on the background and interest of the students.The content of each particular course can then be tuned to diverse student profiles by putting more emphasis in some of the goals, without losing the global view of personalization as a component of a global e-marketing strategy. Rule-base personalization systems represent an appropriate case for introducing the technical content of personalization, and marketing oriented content should be approached from a clear concept of relationship value, as the key concept from which marketing tactics and techniques are derived.

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A personalized recommendation procedure for Internet shopping support

Cited by 101 publications

References 13 publications

Development of an e-Learning Recommender System Using Discrete Choice Models and Bayesian Theory: A Pilot Case in the Shipping Industry

Development of an e-Learning Recommender System Using Discrete Choice Models and Bayesian Theory: A Pilot Case in the Shipping Industry

Applying Fuzzy Logic to Recommend Consumer Electronics

Teaching e-Commerce Personalization Technology: The Need for a Comprehensive View

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