C4.5: programs for machine learning
C4.5: programs for machine learning
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Feature weighting in content based recommendation system using social network analysis
Proceedings of the 17th international conference on World Wide Web
Research on Trust-Aware Recommender Model Based on Profile Similarity
ISCID '08 Proceedings of the 2008 International Symposium on Computational Intelligence and Design - Volume 01
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An Improved Trust Metric for Trust-Aware Recommender Systems
ETCS '09 Proceedings of the 2009 First International Workshop on Education Technology and Computer Science - Volume 01
Producing timely recommendations from social networks through targeted search
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
A personalized recommendation system based on product taxonomy for one-to-one marketing online
Expert Systems with Applications: An International Journal
A study on applying context-aware technology on hypothetical shopping advertisement
Information Systems Frontiers
Analysis of MySpace user profiles
Information Systems Frontiers
Global budgets for local recommendations
Proceedings of the fourth ACM conference on Recommender systems
Aggregating preference graphs for collaborative rating prediction
Proceedings of the fourth ACM conference on Recommender systems
Online evolutionary collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Factorization models for context-/time-aware movie recommendations
Proceedings of the Workshop on Context-Aware Movie Recommendation
Simple time-biased KNN-based recommendations
Proceedings of the Workshop on Context-Aware Movie Recommendation
Predicting most rated items in Weekly Recommendation with temporal regression
Proceedings of the Workshop on Context-Aware Movie Recommendation
Information and knowledge management in online rich presence services
Information Systems Frontiers
| Hi-index | 0.00 |
Recommendation Services (RS) are an essential part of online marketing campaigns. They make it possible to automatically suggest advertisements and promotions that fit the interests of individual users. Social networking websites, and the Web 2.0 in general, offer a collaborative online platform where users socialize, interact and discuss topics of interest with each other. These websites have created an abundance of information about users and their interests. The computational challenge however is to analyze and filter this information in order to generate useful recommendations for each user. Collaborative Filtering (CF) is a recommendation service technique that collects information from a user's preferences and from trusted peer users in order to infer a new targeted suggestion. CF and its variants have been studied extensively in the literature on online recommending, marketing and advertising systems. However, most of the work done was based on Web 1.0, where all the information necessary for the computations is assumed to always be completely available. By contrast, in the distributed environment of Web 2.0, such as in current social networks, the required information may be either incomplete or scattered over different sources. In this paper, we propose the Multi-Collaborative Filtering Trust Network algorithm, an improved version of the CF algorithm designed to work on the Web 2.0 platform. Our simulation experiments show that the new algorithm yields a clear improvement in prediction accuracy compared to the original CF algorithm.