A Method for Aggregating Ranked Services for Personal Preference Based Selection

Typically, users' service requests, which are similar with varying preferences on non-functional attributes, may result in ranked lists of services that partially meet their needs due to conflicting nonfunctional attributes. The resultant multiple ranked lists of services that partially satisfies the user's request makes it challenging for the user to choose an optimal service, based on his/her preference. This work proposes a method that aggregates multiple ranked lists of services into a single aggregated ranked list, where top ranked services are selected for the user. Two algorithms are proposed; 1) Rank Aggregation for Complete Lists (RACoL), that aggregates complete ranked lists and 2) Rank Aggregation for Incomplete Lists (RAIL) to aggregate incomplete ranked lists. Examples using real-world airline services to evaluate both algorithms show that the results from both proposed algorithms closely represent the sets of ranked lists better than using alternative approaches. Experiments were also carried out to validate their performance.