Over the past few years, decentralization of multi-expert systems has become an important research area. These systems do not depend on a central control unit, which enables the control and assignment of distributed, asynchronous, and robust tasks. In most cases, each expert evaluates a small subset of the candidate proposals. The expert is then faced with the challenge of creating an overall "consensus" ranking on the basis of many partial rankings.

The goal of this textbook is to present the key algorithms and theories that introduce recommender systems, group recommender systems, rank aggregation techniques, and genetic algorithms. Web-based recommender systems are the most illustrious application of web personalization to deal with problems of information overload. In this book, the group recommender system uses different rank aggregation strategies to make a recommendation by considering each individual’s preferences. Among various aggregation strategies, Kemeny optimal aggregation, spearman foot rule distance, and Kendall tau distance with bucket order are used.