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
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
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
Combining fuzzy AHP with MDS in identifying the preference similarity of alternatives
Applied Soft Computing
Clustering people according to their preference criteria
Expert Systems with Applications: An International Journal
Collaborative recommender systems: Combining effectiveness and efficiency
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Modeling user multiple interests by an improved GCS approach
Expert Systems with Applications: An International Journal
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
RecoMap: an interactive and adaptive map-based recommender
Proceedings of the 2010 ACM Symposium on Applied Computing
Effective hybrid recommendation combining users-searches correlations using tensors
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Electronic Commerce Research and Applications
A trust-semantic fusion-based recommendation approach for e-business applications
Decision Support Systems
A diffusion mechanism for social advertising over microblogs
Decision Support Systems
A personalized trustworthy seller recommendation in an open market
Expert Systems with Applications: An International Journal
Lattice navigation for collaborative filtering by means of (fuzzy) formal concept analysis
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Contents Recommendation Method Using Social Network Analysis
Wireless Personal Communications: An International Journal
Hi-index | 12.06 |
Recommender systems are powerful tools that allow companies to present personalized offers to their customers and defined as a system which recommends an appropriate product or service after learning the customers' preferences and desires. Extracting users' preferences through their buying behavior and history of purchased products is the most important element of such systems. Due to users' unlimited and unpredictable desires, identifying their preferences is very complicated process. In most researches, less attention has been paid to user's preferences varieties in different product categories. This may decrease quality of recommended items. In this paper, we introduced a technique of recommendation in the context of online retail store which extracts user preferences in each product category separately and provides more personalized recommendations through employing product taxonomy, attributes of product categories, web usage mining and combination of two well-known filtering methods: collaborative and content-based filtering. Experimental results show that proposed technique improves quality, as compared to similar approaches.