Web service selection problem focuses on how to choose component Web services to satisfy user¡¦s non-functional (or QoS) need, and it has been extensively studied in the past. The QoS measures include reliability, response time, and execution cost. However, in some applications, execution result, as demonstrated on some output values, matters, and this is seldom addressed by previous researches. In our work, we proposed an approach to guide the WS selection with the goal to meet user¡¦s preferences on the composition execution outcome. In addition, we consider the partner relationship between Web services. Some partner Web services may produce more desired execution result, such as better quality or a discount, than others. In our approach, we use Bayesian Network to guide Web services selection. Specifically, we propose two Bayesian Network-based methods: Partner-first Bayesian Network and Probability-first Bayesian Network. Both methods rank Web services by considering user¡¦s preference, user¡¦s input variables, and the past execution results of Web services. The experiment result shows that the proposed Bayesian Network methods perform better than the other more straightforward methods.