Cross domain recommendation method is proposed by integrating Fuzzy Analytic Hierarchy Process (AHP) and fuzzy inference method to be applied in Bibliographic Big Data. Existing cross-domain recommendation tackles the problem of sparsity, serendipity, and individual issues found in single-domain, therefore the combination of fuzzy AHP and fuzzy inference method may be able to provide recommendations with a degree of connectedness between domains to initiate transdisciplinary collaborations. The cross domain recommendation will set a stage for efficient preparation for researchers who are interested to venture into other domains and disciplines to increase their research competency. The proposed method is applied to the DBLP bibliographic citation dataset that consists of 10 domains in the computer science discipline. Results show that the combination of fuzzy AHP and FIS as the multi-criteria decision making method is able to provide helpful guide for individuals who are interested in transdisciplinary collaborations to find matching and highly related domains they can collaborate with. Representation of the highly related domains is created using fuzzy visualization technique to overcome uncertainties in the matching result. The target users for the application of this method are individuals educated and knowledgeable in different disciplines, such as computer scientists, biologists, natural disaster experts, urban planners and more.