TALMUD: transfer learning for multiple domains

Authors:
Orly Moreno;Bracha Shapira;Lior Rokach;Guy Shani
Affiliations:
Ben-Gurion University of the Negev and Deutsche Telekom Laboratories, Beer-Sheva, Israel;Ben-Gurion University of the Negev and Deutsche Telekom Laboratories, Beer-Sheva, Israel;Ben-Gurion University of the Negev and Deutsche Telekom Laboratories, Beer-Sheva, Israel;Ben-Gurion University of the Negev and Deutsche Telekom Laboratories, Beer-Sheva, Israel
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

Citing 13
Cited 1

Overfitting and undercomputing in machine learning

ACM Computing Surveys (CSUR)
Link mining: a survey

ACM SIGKDD Explorations Newsletter
Orthogonal nonnegative matrix t-factorizations for clustering

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Co-clustering based classification for out-of-domain documents

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic-bridged PLSA for cross-domain text classification

Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Cross-Domain Mediation in Collaborative Filtering

UM '07 Proceedings of the 11th international conference on User Modeling
Transfer learning for collaborative filtering via a rating-matrix generative model

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Latent class models for collaborative filtering

IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Can movies and books collaborate?: cross-domain collaborative filtering for sparsity reduction

IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A Survey on Transfer Learning

IEEE Transactions on Knowledge and Data Engineering
Empirical analysis of predictive algorithms for collaborative filtering

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Cross-Domain Collaborative Filtering: A Brief Survey

ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
Setting up alternating least squares and iterative majorization algorithms for solving various matrix optimization problems

Computational Statistics & Data Analysis

Nonparametric bayesian multitask collaborative filtering

Proceedings of the 22nd ACM international conference on Conference on information & knowledge management

Quantified Score

Hi-index	0.00

Visualization

Abstract

Most collaborative Recommender Systems (RS) operate in a single domain (such as movies, books, etc.) and are capable of providing recommendations based on historical usage data which is collected in the specific domain only. Cross-domain recommenders address the sparsity problem by using Machine Learning (ML) techniques to transfer knowledge from a dense domain into a sparse target domain. In this paper we propose a transfer learning technique that extracts knowledge from multiple domains containing rich data (e.g., movies and music) and generates recommendations for a sparse target domain (e.g., games). Our method learns the relatedness between the different source domains and the target domain, without requiring overlapping users between domains. The model integrates the appropriate amount of knowledge from each domain in order to enrich the target domain data. Experiments with several datasets reveal that, using multiple sources and the relatedness between domains improves accuracy of results.