Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
Computers and Industrial Engineering
TV Scout: Lowering the Entry Barrier to Personalized TV Program Recommendation
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Personalized e-learning system using Item Response Theory
Computers & Education
Mining learner profile utilizing association rule for web-based learning diagnosis
Expert Systems with Applications: An International Journal
Personalized web-based tutoring system based on fuzzy item response theory
Expert Systems with Applications: An International Journal
Using a style-based ant colony system for adaptive learning
Expert Systems with Applications: An International Journal
Mining e-Learning domain concept map from academic articles
Computers & Education
A blog-based dynamic learning map
Computers & Education
Intelligent web-based learning system with personalized learning path guidance
Computers & Education
A Hybrid System: Neural Network with Data Mining in an e-Learning Environment
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
On-line personalized sales promotion in electronic commerce
Expert Systems with Applications: An International Journal
Personalized curriculum sequencing utilizing modified item response theory for web-based instruction
Expert Systems with Applications: An International Journal
Mining mobility data to minimise travellers' spending on public transport
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
TV scout: lowering the entry barrier to personalized TV program recommendation
From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments
Expert Systems with Applications: An International Journal
| Hi-index | 4.11 |
When information is abundant, the knowledge of which information is useful and valuable matters most. We all use our network of family, friends, and colleagues to recommend movies, books, cars, and news articles. Collaborative filtering technology automates the process of sharing opinions on the relevance and duality of information. Collaborative filtering is one technique among many information filtering techniques that range from unfiltered to personalized and from effortless to laborious. Libraries or the Web are good examples of unfiltered information sources. E-mail directed to one recipient is a good example of a filtered information source. A best-seller list requires little effort fur the user, but provides the same recommendations to all users. Filters based on demographics, such as age, sex, or marital status, require some effort from the user in providing the demographics, and provide some level of personal filtering, so they are near the middle of the chart. Collaborative filtering requires relatively little effort from the user, and provides individually targeted recommendations, so it is in the upper right of the chart. Effort, of course, can be reduced via automation. While collaborative filtering is not necessarily effortless, it requires a relatively small amount of effort on the part of the user and provides very individualized recommendations. The collaborative filtering systems that we discuss here each offer a high degree of personalization, but each system takes a different approach to automation, attempting to find the best trade-off between the amount of work the users must put into the system and the perceived value and benefits they receive in return