Fab: content-based, collaborative recommendation
Communications of the ACM
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
Automatic personalization based on Web usage mining
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Expert-Driven Validation of Rule-Based User Models in Personalization Applications
Data Mining and Knowledge Discovery
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
Building a Recommender Agent for e-Learning Systems
ICCE '02 Proceedings of the International Conference on Computers in Education
ACM Transactions on Information Systems (TOIS)
Enabling Dynamic Content Caching in Web Portals
RIDE '04 Proceedings of the 14th International Workshop on Research Issues on Data Engineering: Web Services for E-Commerce and E-Government Applications (RIDE'04)
Integrating AHP and data mining for product recommendation based on customer lifetime value
Information and Management
IEEE Transactions on Knowledge and Data Engineering
WordNet-based User Profiles for Neighborhood Formation in Hybrid Recommender Systems
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
A new approach for combining content-based and collaborative filters
Journal of Intelligent Information Systems
A collaborative filtering framework based on fuzzy association rules and multiple-level similarity
Knowledge and Information Systems
Collaborative Filtering for Multi-class Data Using Belief Nets Algorithms
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
A new restoration-based recommender system for shopping buddy smart carts
International Journal of Business Information Systems
Expert Systems with Applications: An International Journal
Cross-representation mediation of user models
User Modeling and User-Adapted Interaction
A personalized recommendation system based on product taxonomy for one-to-one marketing online
Expert Systems with Applications: An International Journal
Feature-based recommendations for one-to-one marketing
Expert Systems with Applications: An International Journal
Content-based recommendation systems
The adaptive web
Hybrid web recommender systems
The adaptive web
Unified collaborative filtering model based on combination of latent features
Expert Systems with Applications: An International Journal
Extending the Bayesian Classifier to a Context-Aware Recommender System for Mobile Devices
ICIW '10 Proceedings of the 2010 Fifth International Conference on Internet and Web Applications and Services
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Expert Systems with Applications: An International Journal
Collaborative error-reflected models for cold-start recommender systems
Decision Support Systems
A study on collaborative recommender system using fuzzy-multicriteria approaches
International Journal of Business Information Systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Adoption of business intelligence systems in Indian fashion retail
International Journal of Business Information Systems
Collaborative user modeling for enhanced content filtering in recommender systems
Decision Support Systems
Input online review data and related bias in recommender systems
Decision Support Systems
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Recommender system technology can present personalised offers to customers of companies. This technology suffers from the cold-start and sparsity problems. On the other hand, in most researches, less attention has been paid to user's preferences varieties in different product categories and also explicit and implicit attributes of products. Since users express their opinions implicitly based on some specific attributes of products, this paper proposes a hybrid recommendation approach based on attributes of products to address these problems. After product category and taxonomy formation and attributes extraction for each category, explicit-based module provides recommendations through naive Bayes classifier. Implicit-based module considers the weight vector of implicit attributes for users as chromosomes in genetic algorithm. This algorithm optimises the weights according to historical rating. Finally, recommendations are generated using the results of two modules. The main contributions are addressing sparsity and cold-start problem using naive Bayes classifier and weight optimisation by genetic algorithm.