Semantically enhanced collaborative filtering based on RSVD

Authors:
Andrzej Szwabe;Michal Ciesielczyk;Tadeusz Janasiewicz
Affiliations:
Institute of Control and Information Engineering, Poznan University of Technology, Poznan, Poland;Institute of Control and Information Engineering, Poznan University of Technology, Poznan, Poland;Institute of Control and Information Engineering, Poznan University of Technology, Poznan, Poland
Venue:
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
Year:
2011

Citing 13
Cited 1

Recommendation as classification: using social and content-based information in recommendation

AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Analysis of recommendation algorithms for e-commerce

Proceedings of the 2nd ACM conference on Electronic commerce
Hybrid Recommender Systems: Survey and Experiments

User Modeling and User-Adapted Interaction
Evaluating collaborative filtering recommender systems

ACM Transactions on Information Systems (TOIS)
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
CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering

IEEE Intelligent Systems
Matrix Factorization Techniques for Recommender Systems

Computer
Recommending new movies: even a few ratings are more valuable than metadata

Proceedings of the third ACM conference on Recommender systems
A unified approach to building hybrid recommender systems

Proceedings of the third ACM conference on Recommender systems
Reflective Random Indexing and indirect inference: A scalable method for discovery of implicit connections

Journal of Biomedical Informatics
Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks

International Journal of Approximate Reasoning
Performance of recommender algorithms on top-n recommendation tasks

Proceedings of the fourth ACM conference on Recommender systems
Alleviating the sparsity problem of collaborative filtering using trust inferences

iTrust'05 Proceedings of the Third international conference on Trust Management

A new user similarity model to improve the accuracy of collaborative filtering

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

We investigate a hybrid recommendation method that is based on two-stage data processing - first dealing with content features describing items, and then handling user behavioral data. The evaluation of the proposed method is oriented on the so-called find-good-items task, rather than on the low-error-of-ratings prediction. We focus on a case of extreme collaborative data sparsity. Our method is a combination of content features preprocessing performed by means of Random Indexing (RI), a reflective retraining of preliminary reduced item vectors according to collaborative filtering data, and vector space optimization based on Singular Value Decomposition (SVD). We demonstrate that such an approach is appropriate in high data sparsity scenarios, which disqualify the use of widely-referenced collaborative filtering methods, and allows to generate more accurate recommendations than those obtained through a hybrid method based on weighted feature combination. Moreover, the proposed solution allows to improve the recommendation accuracy without increasing the computational complexity.