Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Improving the Scalability of Multi-Agent Systems
Revised Papers from the International Workshop on Infrastructure for Multi-Agent Systems: Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems
| Hi-index | 0.00 |
In this paper we propose an improved E-Learner communities self-organizing algorithm based on Hebbian Learning Law, which can automatically group distributed e-learners with similar interests and make proper recommendations. Through similarity discovery, trust weights update and potential neighbors adjustment, the algorithm implements an automatic-adapted trust relationship with gradually enhanced satisfactions. It avoids difficult design work required for user preference representation or user similarity calculation. Hence it is suitable for open and distributed e-learning environments. Experimental results have shown that the algorithm has preferable prediction accuracy and user satisfaction. In addition, we achieve an improvement on both satisfaction and scalability.