Service Recommendation: Similarity-Based Representative Skyline

Skyline attracts more and more attention from academic circle and industrial circle because of its application in multi-criterion decision support, preference answering and data analysis. However, it seems unnecessary to recommend all services in skyline while the number of skyline points is large. The number of services in skyline is always large for the reason that comparability decreases with the increase of data dimensionality. Users always want to get only 2 or 3 recommendations instead of all services in skyline. Motivated by this, we propose to compute the representative skyline which contains some points that best describe the contour of the full skyline. In this paper, we propose a new definition which we call “similarity-based representative skyline”. We provide an algorithm SBRSA, which is based on a traversal approach to compute the value of similarity. In particular, we propose an algorithm to maintain the result of SBRSA in dynamic data environment. An extensive performance study using real and synthetic service data is reported to verify its great performance in representation and computing cost.