Fab: content-based, collaborative recommendation
Communications of the ACM
Mineral identification using artificial neural networks and the rotating polarizer stage
Computers & Geosciences - Geological Applications of Digital Imaging
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Computational & Mathematical Organization Theory
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
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
A personalized English learning recommender system for ESL students
Expert Systems with Applications: An International Journal
Information Processing and Management: an International Journal
A collaborative recommender system based on probabilistic inference from fuzzy observations
Fuzzy Sets and Systems
Preference-Based Organization Interfaces: Aiding User Critiques in Recommender Systems
UM '07 Proceedings of the 11th international conference on User Modeling
Handling sequential pattern decay: Developing a two-stage collaborative recommender system
Electronic Commerce Research and Applications
Developing a collective intelligence application for special education
Decision Support Systems
Recommender system based on workflow
Decision Support Systems
In situ evaluation of recommender systems: Framework and instrumentation
International Journal of Human-Computer Studies
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
A recommender system based on tag and time information for social tagging systems
Expert Systems with Applications: An International Journal
Recommender Systems Handbook
Recommender Systems: An Introduction
Recommender Systems: An Introduction
Categorising social tags to improve folksonomy-based recommendations
Web Semantics: Science, Services and Agents on the World Wide Web
Collaborative user modeling with user-generated tags for social recommender systems
Expert Systems with Applications: An International Journal
Sem-Fit: A semantic based expert system to provide recommendations in the tourism domain
Expert Systems with Applications: An International Journal
Hybrid personalized recommender system using centering-bunching based clustering algorithm
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Property-based collaborative filtering for health-aware recommender systems
Expert Systems with Applications: An International Journal
Semantic inference of user's reputation and expertise to improve collaborative recommendations
Expert Systems with Applications: An International Journal
SOMAR: A SOcial Mobile Activity Recommender
Expert Systems with Applications: An International Journal
A framework for collaborative filtering recommender systems
Expert Systems with Applications: An International Journal
RESYGEN: A Recommendation System Generator using domain-based heuristics
Expert Systems with Applications: An International Journal
iTravel: A recommender system in mobile peer-to-peer environment
Journal of Systems and Software
| Hi-index | 12.05 |
Collective intelligence (CI) is an active field of research, which capitalizes the knowledge of human collectives in order to create, to innovate and to invent. There are two important mechanisms to implement CI: recommender and reputation systems. Recommender systems are used to provide filtered information from a large amount of elements. The recommendations are intended to provide interesting elements to users. Recommendation systems can be developed using different techniques and algorithms where the selection of these techniques depends on the area in which they will be applied. This work presents iPixel Recommender Engine, which is focused on the medical field. iPixel Recommendation Engine supports the process of differential diagnosis by recommending mammographic evaluations. Each mammogram is collectively tagged by the users' community with a semantic sense; this feature allows iPixel acquires collective knowledge. iPixel can associate more than one feature with each mammogram. This work also presents a qualitative evaluation, where the basic features that a recommendation system should have in the medical field were obtained. Finally, a comparison was carried out with other similar recommender systems in order to know the Pixel advantages.