Personalization in E-Commerce Applications

Goy, Anna; Ardissono, Liliana; Petrone, G.

doi:10.1007/978-3-540-72079-9_16

Cited by 74 publications

(42 citation statements)

References 104 publications

(106 reference statements)

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“…Moreover, it should be highlighted that the changes have also occurred in market sphere of communication exchange between companies and their consumers, so nowadays physical and electronic market can be considered to be equal when it comes to communication and exchange systems. Furthermore, it is crucial to know the fact that contemporary buyers and clients oft en require personalized approach to satisfy their needs, especially when it comes to services [4].…”

Section: Doing Business In 21mentioning

confidence: 99%

Marketing Activities of Bookkeeping Agencies in the Republic of Serbia

Alčaković¹,

Stanković²,

Mizdraković³

et al. 2014

Proceedings of the 1st International Scientific Conference - FINIZ 2014

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Section: Doing Business In 21mentioning

confidence: 99%

Marketing Activities of Bookkeeping Agencies in the Republic of Serbia

Alčaković¹,

Stanković²,

Mizdraković³

et al. 2014

Proceedings of the 1st International Scientific Conference - FINIZ 2014

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“…By reading this volume you will gain a detailed understanding of how personalisation techniques have been explored in a wide variety of task contexts, from searching and navigating the Web (see Chapter 6 [61] in this book) to e-commerce (see Chapter 16 [38] in this book), healthcare (see Chapter 15 [23]) and e-learning (Chapter 1 [18] in this book) applications.…”

Section: Getting Personalmentioning

confidence: 99%

“…They have been especially useful in many e-commerce domains with many stores using recommendation technologies to help convert browsers into buyers by providing intelligent and timely sales support and product suggestions; see for example Chapter 16 of this book [38] for a survey of recommendation techniques in an e-commerce setting. One of the key features of many recommendation technologies is the ability to consider the needs and preferences of the individual when it comes to generating personalized recommendations or suggestions.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, we will look at how case-based recommenders deal with the issues highlighted above in terms of their approach to selection similarity, recommendation diversity, and the provision of flexible feedback options. In addition we will consider the use of case-based recommendation techniques to produce suggestions that are personalized for the needs of the individual user and in this way present case-based approaches as one important solution for Web personalization problems; see also Chapters 2 [35], 3 [62], and 16 [38] in this book for related work in the area of Web personalization.…”

Section: Introductionmentioning

confidence: 99%

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Case-Based Recommendation

Smyth

Lecture Notes in Computer Science

155

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Abstract.Recommender systems try to help users access complex information spaces. A good example is when they are used to help users to access online product catalogs, where recommender systems have proven to be especially useful for making product suggestions in response to evolving user needs and preferences. Case-based recommendation is a form of content-based recommendation that is well suited to many product recommendation domains where individual products are described in terms of a well defined set of features (e.g., price, colour, make, etc.). These representations allow case-based recommenders to make judgments about product similarities in order to improve the quality of their recommendations and as a result this type of approach has proven to be very successful in many e-commerce settings, especially when the needs and preferences of users are ill-defined, as they often are. In this chapter we will describe the basic approach to case-based recommendation, highlighting how it differs from other recommendation technologies, and introducing some recent advances that have led to more powerful and flexible recommender systems. IntroductionRecently I wanted to buy a new digital camera. I had a vague idea of what I wanted-a 6 mega-pixel digital SLR from a good manufacturer-but it proved difficult and time consuming to locate a product online that suited my needs, especially as these needs evolved during my investigations. Many online stores allowed me to browse or navigate through their product catalog by choosing from a series of static features (e.g., manufacturer, camera type, resolution, level of zoom etc.). Each time I selected a feature I was presented with the set of cameras with this feature and I could then go on to choose another feature to further refine the presented products. Other stores allowed me to search for my ideal camera by entering a query (e.g. "digital slr, 6 mega-pixels") and presented me with a list of results which I could then browse at my leisure.Both of these access options were helpful in different ways-in the beginning I preferred to browse through catalogs but, after getting a feel for the various features and compromises, I tended to use search-based interfaces-however neither provided P. Brusilovsky,

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“…Earlier chapters of this book provided a good overview of personalization techniques for all major paradigms of information access: information retrieval in Chapters 6 [48] and 7 [47] of this book, browsing in Chapters 8 [8], and filtering/recommendation in Chapters 9 [56], 10 [52], 11 [59], 12 [16]. Successful application of personalization techniques has been achieved in such application areas as news access and e-commerce, covered in Chapters 18 [4] and 16 [28] of this book. Education, however, remains resistant to successful development while simultaneously being one of the few areasaccompanying medicine and public health -where the provision of personalized access is most important for users and society.…”

Section: Introductionmentioning

confidence: 99%

Open Corpus Adaptive Educational Hypermedia

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Abstract. Despite the fact that adaptive hypermedia techniques have proven their ability to provide user guidance and orientation in hyperspace, we do not currently see the widespread adoption of these techniques. A couple of reasons may explain this phenomenon. One of them is the current lack of re-usability and interoperability between adaptive techniques/systems, which -to some degree -originates in the so-called "open corpus problem" found in adaptive hypermedia. In this article, we analyze this problem in a popular arena: adaptive hypermedia systems with an emphasis on education. The origins and effects of the open corpus problem are discussed, and recent approaches are demonstrated that have -in one way or the other -developed as strategies for solving the open corpus problem. We summarize these findings and discuss how solution strategies can be successfully employed in the future, enabling adaptive hypermedia techniques within open, dynamic information spaces, such as the Semantic Web.

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Personalization in E-Commerce Applications

Cited by 74 publications

References 104 publications

Marketing Activities of Bookkeeping Agencies in the Republic of Serbia

Marketing Activities of Bookkeeping Agencies in the Republic of Serbia

Case-Based Recommendation

Open Corpus Adaptive Educational Hypermedia

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