Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
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
A Hybrid Recommender System Combining Collaborative Filtering with Neural Network
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
A Hybrid Movie Recommender System Based on Neural Networks
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering
IEEE Intelligent Systems
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic music recommender considering user opinions and audio features
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
Trust-inspiring explanation interfaces for recommender systems
Knowledge-Based Systems
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
IEEE Intelligent Systems
Proposing an ESL recommender teaching and learning system
Expert Systems with Applications: An International Journal
Addressing cold-start problem in recommendation systems
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Using back-propagation to learn association rules for service personalization
Expert Systems with Applications: An International Journal
Evaluation of recommender systems: A new approach
Expert Systems with Applications: An International Journal
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
TREPPS: A Trust-based Recommender System for Peer Production Services
Expert Systems with Applications: An International Journal
Short communication: Recommendation based on rational inferences in collaborative filtering
Knowledge-Based Systems
Exploiting Item Taxonomy for Solving Cold-Start Problem in Recommendation Making
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
A Hybrid Recommender Approach Based on Widrow-Hoff Learning
FGCN '08 Proceedings of the 2008 Second International Conference on Future Generation Communication and Networking - Volume 01
Collaborative filtering adapted to recommender systems of e-learning
Knowledge-Based Systems
Hybrid Recommender System Using Latent Features
WAINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications Workshops
Collaborative Filtering Based on Demographic Attribute Vector
FCC '09 Proceedings of the 2009 ETP International Conference on Future Computer and Communication
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
Improved trust-aware recommender system using small-worldness of trust networks
Knowledge-Based Systems
A classification-based review recommender
Knowledge-Based Systems
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
The agile improvement of MMORPGs based on the enhanced chaotic neural network
Knowledge-Based Systems
Collaborative error-reflected models for cold-start recommender systems
Decision Support Systems
Expert Systems with Applications: An International Journal
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A collaborative filtering similarity measure based on singularities
Information Processing and Management: an International Journal
Providing Justifications in Recommender Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A framework for collaborative filtering recommender systems
Expert Systems with Applications: An International Journal
Identifying patterns in learner's behavior Using Markov chains and n-gram models
CSCC'11 Proceedings of the 2nd international conference on Circuits, Systems, Communications & Computers
Privacy-preserving SOM-based recommendations on horizontally distributed data
Knowledge-Based Systems
A balanced memory-based collaborative filtering similarity measure
International Journal of Intelligent Systems
An effective recommendation method for cold start new users using trust and distrust networks
Information Sciences: an International Journal
A hybrid recommendation approach for a tourism system
Expert Systems with Applications: An International Journal
Information Processing and Management: an International Journal
Knowledge-Based Systems
Silence is also evidence: interpreting dwell time for recommendation from psychological perspective
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Measuring and addressing the impact of cold start on associative tag recommenders
Proceedings of the 19th Brazilian symposium on Multimedia and the web
A Monte Carlo algorithm for cold start recommendation
Proceedings of the 23rd international conference on World wide web
A new user similarity model to improve the accuracy of collaborative filtering
Knowledge-Based Systems
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The new user cold start issue represents a serious problem in recommender systems as it can lead to the loss of new users who decide to stop using the system due to the lack of accuracy in the recommendations received in that first stage in which they have not yet cast a significant number of votes with which to feed the recommender system's collaborative filtering core. For this reason it is particularly important to design new similarity metrics which provide greater precision in the results offered to users who have cast few votes. This paper presents a new similarity measure perfected using optimization based on neural learning, which exceeds the best results obtained with current metrics. The metric has been tested on the Netflix and Movielens databases, obtaining important improvements in the measures of accuracy, precision and recall when applied to new user cold start situations. The paper includes the mathematical formalization describing how to obtain the main quality measures of a recommender system using leave-one-out cross validation.