NDLI: A model-based collaborative filtering method for bounded support data

Content Provider	IEEE Xplore Digital Library
Author	Zhanyu Ma Leijon, A.
Copyright Year	2012
Description	Author affiliation: KTH - Royal Institute of Technology, School of Electrical Engineering Sound and Image Processing Lab., Stockholm, Sweden (Zhanyu Ma; Leijon, A.)
Abstract	Collaborative filtering (CF) is an important technique used in some recommendation systems. The task of CF is to estimate the persons' preferences (e.g., ratings) or to predict the preferences for the future, based on some already known persons' preferences. In general, the model-based CF performs better than the memory-based CF, especially for highly sparse data. In this paper, we present a new model-based CF method for bounded support data, which takes into account the facts that the ratings are usually in a limited interval. A nonnegative matrix factorization (NMF) model is applied to investigate and learn the patterns hidden in the observed data matrix. Each rating value is assumed to be beta distributed and we assign the gamma prior to the parameters in a beta distribution for the purpose of Bayesian estimation. With variation inference framework and some lower bound approximations, an analytically tractable solution can be obtained for the proposed NMF model. By comparing with several existing low-rank matrix approximation methods, the good performance of the proposed method is demonstrated.
Starting Page	545
Ending Page	548
File Size	628112
Page Count	4
File Format	PDF
ISBN	9781467322010
e-ISBN	9781467322041
DOI	10.1109/ICNIDC.2012.6418813
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Beta distribution Filtering Nonnegative matrix factorization Extended factorized approximation Predictive models Approximation methods Sparse matrices Gamma distribution Bayesian methods Collaborative filtering Collaboration Data models Bounded support data Variational inference
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