NDLI: Predicting labels for dyadic data

Content Provider	SpringerLink
Author	Men, Aditya Krishna Elkan, Charles
Copyright Year	2010
Abstract	In dyadic prediction, the input consists of a pair of items (a dyad), and the goal is to predict the value of an observation related to the dyad. Special cases of dyadic prediction include collaborative filtering, where the goal is to predict ratings associated with (user, movie) pairs, and link prediction, where the goal is to predict the presence or absence of an edge between two nodes in a graph. In this paper, we study the problem of predicting labels associated with dyad members. Special cases of this problem include predicting characteristics of users in a collaborative filtering scenario, and predicting the label of a node in a graph, which is a task sometimes called within-network classification or relational learning. This paper shows how to extend a recent dyadic prediction method to predict labels for nodes and labels for edges simultaneously. The new method learns latent features within a log-linear model in a supervised way, to maximize predictive accuracy for both dyad observations and item labels. We compare the new approach to existing methods for within-network classification, both experimentally and analytically. The experiments show, surprisingly, that learning latent features in an unsupervised way is superior for some applications to learning them in a supervised way.
Starting Page	327
Ending Page	343
Page Count	17
File Format	PDF
ISSN	13845810
Journal	Data Mining and Knowledge Discovery
Volume Number	21
Issue Number	2
e-ISSN	1573756X
Language	English
Publisher	Springer US
Publisher Date	2010-07-27
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Dyadic prediction Collaborative filtering Link prediction Social networks Within-network classification Relational learning Information Storage and Retrieval Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Statistics Computing Methodologies Data Mining and Knowledge Discovery Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Information Systems Computer Science Applications Computer Networks and Communications

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 Libarray 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

Predicting labels for dyadic data

CrossClus: user-guided multi-relational clustering

Time to CARE: a collaborative engine for practical disease prediction

Relational peculiarity-oriented mining

Privacy in Data Mining

Learning bidirectional asymmetric similarity for collaborative filtering via matrix factorization

Mining functional dependencies from data

Future trends in data mining

Medical data mining: insights from winning two competitions

Predicting labels for dyadic data

Similar Documents

Predicting labels for dyadic data

CrossClus: user-guided multi-relational clustering

Time to CARE: a collaborative engine for practical disease prediction

Relational peculiarity-oriented mining

Privacy in Data Mining

Learning bidirectional asymmetric similarity for collaborative filtering via matrix factorization

Mining functional dependencies from data

Future trends in data mining

Medical data mining: insights from winning two competitions

Predicting labels for dyadic data