NDLI: Dynamic updating of online recommender systems via feed-forward controllers

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Dynamic updating of online recommender systems via feed-forward controllers

Content Provider	ACM Digital Library
Author	Zanardi, Valentina Capra, Licia
Abstract	Recommender systems have become an essential software component of many online businesses, supporting customers in finding the items (e.g., books on Amazon, movies on Netflix, songs on Last.fm) they are interested in. Key to their success is the level of accuracy they achieve: the more precisely they can predict how much a customer will enjoy an item, the higher the profit that the business will make (e.g., in terms of more purchases). In quantifying the accuracy of recommender systems, the evaluation methodology followed by researchers has so far neglected an important aspect: that these businesses grow continuously over time, both in terms of users and items. The data structures used by the recommender system to compute predictions become stale and thus have to be updated regularly. Intuitively, the more often the data structures are being updated, the higher the accuracy achieved, but the higher the computational cost afforded, because of the extremely large volume of data being handled. System administrators often perform the update at fixed intervals of time (e.g., weekly, fortnightly), in an effort to balance accuracy versus cost. We argue that such an approach benefits neither accuracy nor cost, as businesses do not grow linearly in time, thus risking the fixed update interval to be at times too coarse (with negative impact on accuracy), and at other times too fine grained (with negative impact on cost). We thus advocate for a self-monitoring and self-adaptive approach, whereby the system monitors its own growth over time, estimates the loss in accuracy it would endure if an update were not being performed based on the observed growth, and dynamically decides whether the benefit of performing an update (accuracy) outweighs its computational cost. Using real data from the Bibsonomy website, we demonstrate how this simple technique enables system administrators to transparently balance these two conflicting requirements.
Starting Page	11
Ending Page	19
Page Count	9
File Format	PDF
ISBN	9781450305754
DOI	10.1145/1988008.1988011
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-05-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Self-monitoring Self-adaptation Recommender systems
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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