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Designing Hybrid Intelligence based Recommendation Algorithms: An Experience through Machine Learning Metaphor
Informatica ( IF 2.9 ) Pub Date : 2020-09-15 , DOI: 10.31449/inf.v44i3.2926
Arup Roy

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.

中文翻译:

设计基于混合智能的推荐算法:通过机器学习比喻的经验

本文对博士论文进行了总结,该论文提出了推荐系统中的高效混合智能算法。开发有效的推荐算法以确保及时推荐质量是一项棘手的任务。此外,传统的推荐系统不足以应对新技术趋势。为了克服这些问题,已经发现了一批复杂的推荐系统,例如上下文推荐、群组推荐和社交推荐。该研究工作使用自然启发算法、进化算法、群体智能算法和机器学习技术来调查和分析新的推荐类型。这些算法解决了这些推荐器的一些关键问题。因此,
更新日期:2020-09-15
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