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Who's Next? Scheduling Personalization Services with Variable Service Times

Who's Next? Scheduling Personalization Services with Variable Service Times Who's Next? Scheduling Personalization Services with Variable Service Times DENGPAN LIU, Iowa State University SUMIT SARKAR, University of Texas at Dallas CHELLIAH SRISKANDARAJAH, Texas A&M University Online personalization has become quite prevalent in recent years, with firms able to derive additional profits from such services. As the adoption of such services grows, firms implementing such practices face some operational challenges. One important challenge lies in the complexity associated with the personalization process and how to deploy available resources to handle such complexity. The complexity is exacerbated when a site faces a large volume of requests in a short amount of time, as is often the case for e-commerce and content delivery sites. In such situations, it is generally not possible for a site to provide perfectly personalized service to all requests. Instead, a firm can provide differentiated service to requests based on the amount of profiling information available about the visitor. We consider a scenario where the revenue function is concave, capturing the diminishing returns from personalization effort. Using a batching approach, we determine the optimal scheduling policy (i.e., time allocation and sequence of service) for a batch that accounts for the externality cost incurred when a request http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Management Information Systems (TMIS) Association for Computing Machinery

Who's Next? Scheduling Personalization Services with Variable Service Times

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2015 by ACM Inc.
ISSN
2158-656X
DOI
10.1145/2764920
Publisher site
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Abstract

Who's Next? Scheduling Personalization Services with Variable Service Times DENGPAN LIU, Iowa State University SUMIT SARKAR, University of Texas at Dallas CHELLIAH SRISKANDARAJAH, Texas A&M University Online personalization has become quite prevalent in recent years, with firms able to derive additional profits from such services. As the adoption of such services grows, firms implementing such practices face some operational challenges. One important challenge lies in the complexity associated with the personalization process and how to deploy available resources to handle such complexity. The complexity is exacerbated when a site faces a large volume of requests in a short amount of time, as is often the case for e-commerce and content delivery sites. In such situations, it is generally not possible for a site to provide perfectly personalized service to all requests. Instead, a firm can provide differentiated service to requests based on the amount of profiling information available about the visitor. We consider a scenario where the revenue function is concave, capturing the diminishing returns from personalization effort. Using a batching approach, we determine the optimal scheduling policy (i.e., time allocation and sequence of service) for a batch that accounts for the externality cost incurred when a request

Journal

ACM Transactions on Management Information Systems (TMIS) – Association for Computing Machinery

Published: Jul 4, 2015

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