Optimizing offer sets based on user profiles

Name: Optimizing offer sets based on user profiles
Author: Atahan, Pelin, Johar, M., Sarkar, S.

İsim	Optimizing offer sets based on user profiles
Yazar	Atahan, Pelin, Johar, M., Sarkar, S.
Basım Tarihi:	2009
Basım Yeri	- Social Science Research Network
Konu	Personalization, Recommendation, E-commerce, Probability theory
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2-s2.0-84903851926
Kayıt Numarası	e5d2ef2a-dc76-4561-bd83-e38873374660
Lokasyon	Sectoral Education and Professional Development
Tarih	2009
Örnek Metin	Personalization and recommendation systems are being increasingly utilized by ecommerce firms to provide personalized product offerings to visitors at the firms’ web sites. These systems often recommend, at each interaction, multiple items (referred to as an offer set) that might be of interest to a visitor. When making recommendations firms typically attempt to maximize their expected payoffs from the offer set. This paper examines how a firm can maximize its expected payoffs by leverag ing th e kn owledge of the profiles of visitors to their site. We provide a methodology that accounts for the interactions among items in an offer set in order to determine the expected payoff. Identifying the optimal offer set is a difficult problem when the number of candidate items to rec ommend is large. We develop an efficient heuristic for this problem, and show that it performs well for both small and large problem instances.

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Optimizing offer sets based on user profiles

Yazar Atahan, Pelin, Johar, M., Sarkar, S.

Basım Tarihi 2009

Basım Yeri - Social Science Research Network

Konu Personalization, Recommendation, E-commerce, Probability theory

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2-s2.0-84903851926

Kayıt Numarası e5d2ef2a-dc76-4561-bd83-e38873374660

Lokasyon Sectoral Education and Professional Development

Tarih 2009

Örnek Metin Personalization and recommendation systems are being increasingly utilized by ecommerce firms to provide personalized product offerings to visitors at the firms’ web sites. These systems often recommend, at each interaction, multiple items (referred to as an offer set) that might be of interest to a visitor. When making recommendations firms typically attempt to maximize their expected payoffs from the offer set. This paper examines how a firm can maximize its expected payoffs by leverag ing th e kn owledge of the profiles of visitors to their site. We provide a methodology that accounts for the interactions among items in an offer set in order to determine the expected payoff. Identifying the optimal offer set is a difficult problem when the number of candidate items to rec ommend is large. We develop an efficient heuristic for this problem, and show that it performs well for both small and large problem instances.