NDLI: A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion

Please wait, while we are loading the content...

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 8

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 7

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 6

Issue 4(Regular Papers and Special Section on Intelligent Healthcare Informatics), August 2015

Issue 3(Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd Intelligence), May 2015

Issue 2(Special Section on Visual Understanding with RGB-D Sensors), May 2015

Visual Understanding with RGB-D Sensors: An Introduction to the Special Issue

Kinect Depth Recovery Using a Color-Guided, Region-Adaptive, and Depth-Selective Framework

Depth Error Elimination for RGB-D Cameras

A Combined Approach Toward Consistent Reconstructions of Indoor Spaces Based on 6D RGB-D Odometry and KinectFusion

Robust Multiview Feature Learning for RGB-D Image Understanding

From RGB-D Images to RGB Images: Single Labeling for Mining Visual Models

Accurate and Robust Moving-Object Segmentation for Telepresence Systems

Fusing Multiple Features for Depth-Based Action Recognition

An Evaluation of Gamesourced Data for Human Pose Estimation

Latent Support Vector Machine Modeling for Sign Language Recognition with Kinect

A Real-Time Hand Posture Recognition System Using Deep Neural Networks

Real-Time System for Driver Fatigue Detection by RGB-D Camera

An Approach to Ballet Dance Training through MS Kinect and Visualization in a CAVE Virtual Reality Environment

Exploring Spatial Correlation for Visual Object Retrieval

Pattern Matching Techniques for Replacing Missing Sections of Audio Streamed across Wireless Networks

An Association-Based Unified Framework for Mining Features and Opinion Words

A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion

Data Mining of Online Genealogy Datasets for Revealing Lifespan Patterns in Human Population

Issue 1, April 2015

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 5

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 4

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 3

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 2

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 1

A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion

Content Provider	ACM Digital Library
Author	Wang, Shuaiqiang Huang, Shanshan Cheng, Peizhe Ma, Jun
Copyright Year	2015
Abstract	Collaborative Filtering (CF) is one of the most successful algorithms in recommender systems. However, it suffers from data sparsity and scalability problems. Although many clustering techniques have been incorporated to alleviate these two problems, most of them fail to achieve further significant improvement in recommendation accuracy. First of all, most of them assume each user or item belongs to a single cluster. Since usually users can hold multiple interests and items may belong to multiple categories, it is more reasonable to assume that users and items can join multiple clusters (groups), where each cluster is a subset of like-minded users and items they prefer. Furthermore, most of the clustering-based CF models only utilize historical rating information in the clustering procedure but ignore other data resources in recommender systems such as the social connections of users and the correlations between items. In this article, we propose HMCoC, a Hybrid Multigroup CoClustering recommendation framework, which can cluster users and items into multiple groups simultaneously with different information resources. In our framework, we first integrate information of user--item rating records, user social networks, and item features extracted from the DBpedia knowledge base. We then use an optimization method to mine meaningful user--item groups with all the information. Finally, we apply the conventional CF method in each cluster to make predictions. By merging the predictions from each cluster, we generate the top-n recommendations to the target users for return. Extensive experimental results demonstrate the superior performance of our approach in top-n recommendation in terms of MAP, NDCG, and F1 compared with other clustering-based CF models.
Starting Page	1
Ending Page	22
Page Count	22
File Format	PDF
ISSN	21576904
e-ISSN	21576912
DOI	10.1145/2700465
Volume Number	6
Issue Number	2
Journal	ACM Transactions on Intelligent Systems and Technology (TIST)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-03-31
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Recommender systems Coclustering Collaborative filtering Data sparsity Information fusion
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Theoretical 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

A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion

VSRank: A Novel Framework for Ranking-Based Collaborative Filtering

Anytime Algorithms for Recommendation Service Providers

A Novel Classification Framework for Evaluating Individual and Aggregate Diversity in Top-N Recommendations

Social temporal collaborative ranking for context aware movie recommendation

A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks

Hybrid Collaborative Filtering algorithm for bidirectional Web service recommendation

Unified Collaborative and Content-Based Web Service Recommendation

An empirical comparison of social, collaborative filtering, and hybrid recommenders

A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion

Similar Documents

A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion

VSRank: A Novel Framework for Ranking-Based Collaborative Filtering

Anytime Algorithms for Recommendation Service Providers

A Novel Classification Framework for Evaluating Individual and Aggregate Diversity in Top-N Recommendations

Social temporal collaborative ranking for context aware movie recommendation

A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks

Hybrid Collaborative Filtering algorithm for bidirectional Web service recommendation

Unified Collaborative and Content-Based Web Service Recommendation

An empirical comparison of social, collaborative filtering, and hybrid recommenders

A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion