An iterative semi-explicit rating method for building collaborative recommender systems

Authors:
Buhwan Jeong;Jaewook Lee;Hyunbo Cho
Affiliations:
Data Mining Team, Daum Communications Corp, 1730-8 Odeung, Jeju 690-150, South Korea;Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), San 31 Hyoja Pohang, Kyungbuk 790-784, South Korea;Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), San 31 Hyoja Pohang, Kyungbuk 790-784, South Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 16
Cited 10

GroupLens: an open architecture for collaborative filtering of netnews

CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
GroupLens: applying collaborative filtering to Usenet news

Communications of the ACM
Item-based collaborative filtering recommendation algorithms

Proceedings of the 10th international conference on World Wide Web
MovieLens unplugged: experiences with an occasionally connected recommender system

Proceedings of the 8th international conference on Intelligent user interfaces
Amazon.com Recommendations: Item-to-Item Collaborative Filtering

IEEE Internet Computing
Item-based top-N recommendation algorithms

ACM Transactions on Information Systems (TOIS)
An Improved Cluster Labeling Method for Support Vector Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving recommendation lists through topic diversification

WWW '05 Proceedings of the 14th international conference on World Wide Web
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

IEEE Transactions on Knowledge and Data Engineering
ParTriCluster: A Scalable Parallel Algorithm for Gene Expression Analysis

SBAC-PAD '06 Proceedings of the 18th International Symposium on Computer Architecture and High Performance Computing
Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
A personalized English learning recommender system for ESL students

Expert Systems with Applications: An International Journal
A novel method for measuring semantic similarity for XML schema matching

Expert Systems with Applications: An International Journal
Classification-based collaborative filtering using market basket data

Expert Systems with Applications: An International Journal
Empirical analysis of predictive algorithms for collaborative filtering

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Equilibrium-Based Support Vector Machine for Semisupervised Classification

IEEE Transactions on Neural Networks

Improving memory-based collaborative filtering via similarity updating and prediction modulation

Information Sciences: an International Journal
Developing an ontology-supported information integration and recommendation system for scholars

Expert Systems with Applications: An International Journal
Collaborative Filtering with a User-Item Matrix Reduction Technique

International Journal of Electronic Commerce
A literature review and classification of recommender systems research

Expert Systems with Applications: An International Journal
A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis

Electronic Commerce Research and Applications
RESYGEN: A Recommendation System Generator using domain-based heuristics

Expert Systems with Applications: An International Journal
A new similarity function for selecting neighbors for each target item in collaborative filtering

Knowledge-Based Systems
A comparison of clustering-based privacy-preserving collaborative filtering schemes

Applied Soft Computing
Exploiting two-faceted web of trust for enhanced-quality recommendations

Expert Systems with Applications: An International Journal
Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction

Pattern Recognition

Quantified Score

Hi-index	12.06

Visualization

Abstract

Collaborative filtering plays the key role in recent recommender systems. It uses a user-item preference matrix rated either explicitly (i.e., explicit rating) or implicitly (i.e., implicit feedback). Despite the explicit rating captures the preferences better, it often results in a severely sparse matrix. The paper presents a novel iterative semi-explicit rating method that extrapolates unrated elements in a semi-supervised manner. Extrapolation is simply an aggregation of neighbor ratings, and iterative extrapolations result in a dense preference matrix. Preliminary simulation results show that the recommendation using the semi-explicit rating data outperforms that of using the pure explicit data only.