NDLI: Anytime algorithms for top-N recommenders

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Anytime algorithms for top-N recommenders

Content Provider	ACM Digital Library
Author	Ben-Shimon, David
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.
Starting Page	463
Ending Page	466
Page Count	4
File Format	PDF
ISBN	9781450324090
DOI	10.1145/2507157.2508074
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Locality sensitive hashing Anytime algorithms Collaborative filtering Item-based
Content Type	Text
Resource Type	Article

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