NDLI: Factor in the neighbors: Scalable and accurate collaborative filtering

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ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 11

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 10

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 9

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 8

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 7

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 6

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 5

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 4

Issue 4, October 2010

Issue 3, October 2010

Issue 2, May 2010

Issue 1, January 2010

Factor in the neighbors: Scalable and accurate collaborative filtering

Motif discovery in physiological datasets: A methodology for inferring predictive elements

Self-sufficient itemsets: An approach to screening potentially interesting associations between items

Mining multidimensional and multilevel sequential patterns

VOGUE: A variable order hidden Markov model with duration based on frequent sequence mining

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 3

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 2

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 1

Factor in the neighbors: Scalable and accurate collaborative filtering

Content Provider	ACM Digital Library
Author	Koren, Yehuda
Copyright Year	2010
Abstract	Recommender systems provide users with personalized suggestions for products or services. These systems often rely on collaborating filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The most common approach to CF is based on neighborhood models, which originate from similarities between products or users. In this work we introduce a new neighborhood model with an improved prediction accuracy. Unlike previous approaches that are based on heuristic similarities, we model neighborhood relations by minimizing a global cost function. Further accuracy improvements are achieved by extending the model to exploit both explicit and implicit feedback by the users. Past models were limited by the need to compute all pairwise similarities between items or users, which grow quadratically with input size. In particular, this limitation vastly complicates adopting user similarity models, due to the typical large number of users. Our new model solves these limitations by factoring the neighborhood model, thus making both item-item and user-user implementations scale linearly with the size of the data. The methods are tested on the Netflix data, with encouraging results.
Starting Page	1
Ending Page	24
Page Count	24
File Format	PDF
ISSN	15564681
e-ISSN	1556472X
DOI	10.1145/1644873.1644874
Volume Number	4
Issue Number	1
Journal	ACM Transactions on Knowledge Discovery from Data (TKDD)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-01-18
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Netflix Prize Recommender systems Collaborative filtering
Content Type	Text
Resource Type	Article
Subject	Computer Science

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

Predictor set optimization for collaborative filtering

Matrix Factorization Techniques for Recommender Systems

Modeling relationships at multiple scales to improve accuracy of large recommender systems

Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems

Interview with Simon Funk

Evaluating collaborative filtering recommender systems

Parallel and Distributed Collaborative Filtering: A Survey

KDD Cup and workshop 2007

The Netflix Recommender System: Algorithms, Business Value, and Innovation

Factor in the neighbors: Scalable and accurate collaborative filtering

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Predictor set optimization for collaborative filtering

Matrix Factorization Techniques for Recommender Systems

Modeling relationships at multiple scales to improve accuracy of large recommender systems

Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems

Interview with Simon Funk

Evaluating collaborative filtering recommender systems

Parallel and Distributed Collaborative Filtering: A Survey

KDD Cup and workshop 2007

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Factor in the neighbors: Scalable and accurate collaborative filtering