Application of Decision-Tree Induction Techniques to Personalized Advertisements on Internet Storefronts

Kim, Jong Woo; Lee, Byung Hun; Shaw, Michael J.; Chang, Hsin-Lu; Nelson, Matthew L.

doi:10.1080/10864415.2001.11044215

Cited by 154 publications

(77 citation statements)

References 13 publications

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“…Personalization is characterized as the degree to which the correspondence amongst buyers and venders is formed to the purchasers' inclinations, needs, and shopping propensities (Zeng et al, 2003). It guarantees that buyers get the most applicable and suitable message (Kim et al 2001). Personalization not just improves their view of the merchant (Xu 2003) additionally diminishes their seeking costs-the push to explore wireless sites (Venkatesh and Ramesh 2006).…”

Section: The Concept Of M-commercementioning

confidence: 99%

Small-scale individual entrepreneurs (SIEs) and the usage of mobile money (M-money) and mobile commerce (M-commerce) in facilitating business growth in Ghana

Amegbe¹,

Hanu²

2017

10.5267/j.msl

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Section: The Concept Of M-commercementioning

confidence: 99%

Small-scale individual entrepreneurs (SIEs) and the usage of mobile money (M-money) and mobile commerce (M-commerce) in facilitating business growth in Ghana

Amegbe¹,

Hanu²

2017

10.5267/j.msl

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“…From any sub-reference group U i, we can also decompose it into two sub-groups by matching between the first element of Pref Ui , denoted as Pref Ui (1) and that of Pref uj of each member u j , denoted as Pref uj (1), as seen in (3).…”

Section: Sub-reference Groups In Binary Decision Tree (Bdt)mentioning

confidence: 99%

“…The rule-based recommendation is usually implemented by a predetermined rule, for instance, ifthen rule. Kim et al [1] proposed a marketing rule extraction technique for personalized recommendation on internet storefronts using tree induction method. As one of representative rule-based techniques, Aggrawall et al [2] proposed a method to identify frequent item sets from the estimated frequency distribution using association-rule mining algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method Providing Multimedia Contents According to Preference Clones in Mobile Environment

Kang

Park

2007

Computational Science – ICCS 2007

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Abstract. We introduce a novel method of automatic providing multimedia content in mobile environment. With using our method, the problem of the limitation of resource of mobile devices can be solved. In this paper, we introduce a novel method of recommendation of personalized contents according to preference clones using a collaborative filtering technique in mobile environment. We divide the user group to two sub-groups by analyzing the match of preferences of members in the sub-groups. The division process recursively applies to each sub-group and place the sub-groups in a binary decision tree (BDT). From the binary decision tree, we identify the preference clones of each target user by matching the target user's consumption behavior to that of each sub-group in the BDT with a sequential manner. We also implemented our system based on Java Micro Edition platform.

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“…Its rapid growth has made both companies and customers face a new dimension. Whereas companies are facing hard time to survive due to more and more competitions, the opportunity for the customers to choose among more and more products have increased the burden of information processing before they select which meet their needs [1,2]. This phenomenon of e-commerce on the internet gives scope to the companies to provide more and more choices for the customers [3].…”

Section: Introductionmentioning

confidence: 99%

Personalized Web Recommendation Combining User-centered Collaborative Technique with URL Weighting

HossainArif¹,

Ullah²,

Habibullah³

et al. 2013

IJCA

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Web usage mining has become very popular in various business areas for learning more about the users' browsing behavior and recommending the perfect product in which the user is interested in. At present there are many systems that recommend for the users on web usage mining, but most of the systems suffer from inappropriate scalability, which would lead to very weak recommendations. In this paper we proposed a new technique that gives emphasis on page view weighting based on transaction timing and building a session pattern graph for each session. This technique provides the scope for better scalability and also provides effective number of recommendations with remarkable accuracy.

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Application of Decision-Tree Induction Techniques to Personalized Advertisements on Internet Storefronts

Cited by 154 publications

References 13 publications

Small-scale individual entrepreneurs (SIEs) and the usage of mobile money (M-money) and mobile commerce (M-commerce) in facilitating business growth in Ghana

Small-scale individual entrepreneurs (SIEs) and the usage of mobile money (M-money) and mobile commerce (M-commerce) in facilitating business growth in Ghana

A Novel Method Providing Multimedia Contents According to Preference Clones in Mobile Environment

Personalized Web Recommendation Combining User-centered Collaborative Technique with URL Weighting

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