Designing Hybrid Intelligence based Recommendation Algorithms: An Experience through Machine Learning Metaphor
This article presents a summarization of the doctoral thesis which proposes efficient hybrid intelligent algorithms in recommendation systems. Development of effective recommendation algorithms for ensuring quality recommendation in timely manner is a tricky task. Moreover, traditional recommendation system is inadequate to cope up with the new technological trends. In order to overcome these issues, a batch of sophisticated recommendation systems has been discovered e.g. contextual recommendation, group recommendation, and social recommendation. The research work, investigates and analyzes new genres of recommenders using nature inspired algorithms, evolutionary algorithms, swarm intelligence algorithms, and machine learning techniques. The algorithms resolve some crucial problems of these recommenders. As a result, more precise personalized recommendation is ensured.
This work is licensed under a Creative Commons Attribution 3.0 License.