C4.5: programs for machine learning
C4.5: programs for machine learning
An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems (TOIS)
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Machine Learning
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Nonlinear Markov networks for continuous variables
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
Maximizing Text-Mining Performance
IEEE Intelligent Systems
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Compression by Model Combination
DCC '98 Proceedings of the Conference on Data Compression
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
The context-tree weighting method: basic properties
IEEE Transactions on Information Theory
Removing redundancy and inconsistency in memory-based collaborative filtering
Proceedings of the eleventh international conference on Information and knowledge management
Probabilistic Memory-Based Collaborative Filtering
IEEE Transactions on Knowledge and Data Engineering
Knowing a tree from the forest: art image retrieval using a society of profiles
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Support vector machines for collaborative filtering
Proceedings of the 44th annual Southeast regional conference
VCR: Virtual community recommender using the technology acceptance model and the user's needs type
Expert Systems with Applications: An International Journal
A probabilistic model for item-based recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
A two-level approach to choose the cost parameter in support vector machines
Expert Systems with Applications: An International Journal
Preference networks: probabilistic models for recommendation systems
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Building an expert travel agent as a software agent
Expert Systems with Applications: An International Journal
Context Dependent Movie Recommendations Using a Hierarchical Bayesian Model
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Adaptive Web SitesA Knowledge Extraction from Web Data Approach
Proceedings of the 2008 conference on Adaptive Web Sites: A Knowledge Extraction from Web Data Approach
Review: Personalizing recommendations for tourists
Telematics and Informatics
Unified collaborative filtering model based on combination of latent features
Expert Systems with Applications: An International Journal
Text mining techniques for leveraging positively labeled data
BioNLP '11 Proceedings of BioNLP 2011 Workshop
A comprehensive reference model for personalized recommender systems
HI'11 Proceedings of the 2011 international conference on Human interface and the management of information - Volume Part I
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Hybrid-ε-greedy for mobile context-aware recommender system
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Crowdsourcing recommendations from social sentiment
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
Identifying well-formed biomedical phrases in MEDLINE® text
Journal of Biomedical Informatics
Personalized expert-based recommender system: training C-SVM for personalized expert identification
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Customized products recommendation based on probabilistic relevance model
Journal of Intelligent Manufacturing
Preference-based mining of top-K influential nodes in social networks
Future Generation Computer Systems
Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems
Expert Systems with Applications: An International Journal
| Hi-index | 0.01 |
Recommender systems use historical data on user preferences and other available data on users (for example, demographics) and items (for example, taxonomy) to predict items a new user might like. Applications of these methods include recommending items for purchase and personalizing the browsing experience on a web-site. Collaborative filtering methods have focused on using just the history of user preferences to make the recommendations. These methods have been categorized as memory-based if they operate over the entire data to make predictions and as model-based if they use the data to build a model which is then used for predictions. In this paper, we propose the use of linear classifiers in a model-based recommender system. We compare our method with another model-based method using decision trees and with memory-based methods using data from various domains. Our experimental results indicate that these linear models are well suited for this application. They outperform a commonly proposed memory-based method in accuracy and also have a better tradeoff between off-line and on-line computational requirements.