Incorporating both qualitative and quantitative preferences for service recommendation.

The aim of service recommendation is to help a service user find an optimal service. Preference-based service recommendation has gained significant popularity in recent years. Existing studies primarily focus on using similarity measures for quantitative preference. None of them has considered both the quantitative and qualitative preference simultaneously. In this paper, we propose to integrate both the quantitative and qualitative preference to calculate the user similarity. We then seek similar users by using user similarity. Finally, service recommendation is performed using the preference information of those similar users based on the idea of collaborative filtering. A series of experiments have been conducted to validate the correctness of our method and comparison with existing approaches demonstrates the effectiveness of our method.