Resource Allocation Policies for Personalization in Content Delivery Sites

One of the distinctive features of sites on the Internet is their ability to gather enormous amounts of information about their visitors and to use this information to enhance a visitor's experience by providing personalized information or recommendations. In providing personalized services, a website is typically faced with the following trade-off: When serving a visitor's request, it can deliver an optimally personalized version of the content to the visitor, possibly with a long delay because of the computational effort needed, or it can deliver a suboptimal version of the content more quickly. This problem becomes more complex when several requests are waiting for information from a server. The website then needs to trade off the benefit from providing more personalized content to each user with the negative externalities associated with higher waiting costs for all other visitors that have requests pending. We examine several deterministic resource allocation policies in such personalization contexts. We identify an optimal policy for the above problem when requests to be scheduled are batched, and show that the policy can be very efficiently implemented in practice. We provide an experimental approach to determine optimal batch lengths, and demonstrate that it performs favorably when compared with viable queueing approaches.