In real world applications, Internet plays a vital role on B2B e-services. B2B e-services in the sense and user can purchase or gaining services through online it could be achieved by giving such recommendations to generate personalized suggestion on product/services to customer but it is complex to handle because of the data in the format of tree structure and also for fuzzy user preference. To handle these problems, we propose a technique to model the fuzzy tree-structured user preferences. And also a recommendation approach is developed for recommending tree-structured items our proposed approach is applied to various datasets like “Australian business dataset” and the “MovieLens dataset”. Our proposed approach shows the effectiveness on user preference profile and excellent performance on our proposed recommendation approach for tree-structured items. The main objective of our framework on making recommendations to personal users. Our proposed framework solves the problem on complicated tree structures data in business applications. E-service intelligence is a new research field that deals with fundamental roles, social impacts and practical applications of various intelligent technologies on the Internet based e-service applications that are provided by e-government, e-business, e-commerce, e-market, e-finance, and e-learning systems, to name a few. This chapter offers a thorough introduction and systematic overview of the new field e-service intelligence mainly based on computational intelligence techniques.