Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Recommendation systems: a probabilistic analysis
Journal of Computer and System Sciences - Special issue on Internet algorithms
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
The InfoFinder Agent: Learning User Interests through Heuristic Phrase Extraction
IEEE Expert: Intelligent Systems and Their Applications
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Nantonac collaborative filtering: recommendation based on order responses
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Journal of Artificial Intelligence Research
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Collaborative filtering based on workflow space
Expert Systems with Applications: An International Journal
Integrating web mining and neural network for personalized e-commerce automatic service
Expert Systems with Applications: An International Journal
An approach to group ranking decisions in a dynamic environment
Decision Support Systems
Aggregating preference graphs for collaborative rating prediction
Proceedings of the fourth ACM conference on Recommender systems
RESYGEN: A Recommendation System Generator using domain-based heuristics
Expert Systems with Applications: An International Journal
Improving recommendation performance through ontology-based semantic similarity
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Recommendations of closed consensus temporal patterns by group decision making
Knowledge-Based Systems
| Hi-index | 12.06 |
In recent years, Collaborative Filtering (CF) has proven to be one of the most successful techniques used in recommendation systems. Since current CF systems estimate the ratings of not-yet-rated items based on other items' ratings, these CF systems fail to recommend products when users' preferences are not expressed in numbers. In many practical situations, however, users' preferences are represented by ranked lists rather than numbers, such as lists of movies ranked according to users' preferences. Therefore, this study proposes a novel collaborative filtering methodology for product recommendation when the preference of each user is expressed by multiple ranked lists of items. Accordingly, a four-staged methodology is developed to predict the rankings of not-yet-ranked items for the active user. Finally, a series of experiments is performed, and the results indicate that the proposed methodology produces high-quality recommendations.