Anytime algorithms for top-N recommenders

Authors:
David Ben-Shimon
Affiliations:
Ben-Gurion University of the Negev, Beer-Sheva, Israel
Venue:
Proceedings of the 7th ACM conference on Recommender systems
Year:
2013

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Abstract

Many small and mid-sized e-businesses use the services of recommender system (RS) provider companies to outsource the construction and maintenance of their RS. The fees that RS providers charge their clients must cover the computation costs for constructing and updating the recommendation model. By using anytime algorithms, a RS provider can control the computation costs and still offer a system capable of delivering reasonable recommendations. Thus, a RS provider should be able to stop the construction of a recommendation model once the cost for compu-ting it reaches the amount the customer has agreed to pay. In this research we suggest anytime algorithms as a possible solu-tion to a problem that RS providers face. We demonstrate how certain existing recommendation algorithms can be adjusted to the anytime framework. We focus on the case of item-item algorithms, showing how the anytime behavior can be improved using different ordering methods of computations. We conduct a comparative study demonstrating the benefits of the proposed methods for top-N item-item recommenders.