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
GroupLens: applying collaborative filtering to Usenet news
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
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web 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)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
VISCORS: A Visual-Content Recommender for the Mobile Web
IEEE Intelligent Systems
IEEE Transactions on Knowledge and Data Engineering
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Unified relevance models for rating prediction in collaborative filtering
ACM Transactions on Information Systems (TOIS)
Using error-correcting dependencies for collaborative filtering
Data & Knowledge Engineering
Boosting collaborative filtering based on statistical prediction errors
Proceedings of the 2008 ACM conference on Recommender systems
TagiCoFi: tag informed collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Error-based collaborative filtering algorithm for top-N recommendation
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
On the stability of recommendation algorithms
Proceedings of the fourth ACM conference on Recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A collaborative filtering approach to mitigate the new user cold start problem
Knowledge-Based Systems
Kernel-Mapping Recommender system algorithms
Information Sciences: an International Journal
A trust-semantic fusion-based recommendation approach for e-business applications
Decision Support Systems
Knowledge-Based Systems
Attribute-based collaborative filtering using genetic algorithm and weighted C-means algorithm
International Journal of Business Information Systems
International Journal of Business Information Systems
International Journal of Business Information Systems
A Monte Carlo algorithm for cold start recommendation
Proceedings of the 23rd international conference on World wide web
| Hi-index | 0.00 |
Collaborative Filtering (CF), one of the most successful technologies among recommender systems, is a system assisting users to easily find useful information. One notable challenge in practical CF is the cold start problem, which can be divided into cold start items and cold start users. Traditional CF systems are typically unable to make good quality recommendations in the situation where users and items have few opinions. To address these issues, in this paper, we propose a unique method of building models derived from explicit ratings and we apply the models to CF recommender systems. The proposed method first predicts actual ratings and subsequently identifies prediction errors for each user. From this error information, pre-computed models, collectively called the error-reflected model, are built. We then apply the models to new predictions. Experimental results show that our approach obtains significant improvement in dealing with cold start problems, compared to existing work.