There is a consensus that U. S. patent examiners, who are responsible for identifying prior art relevant to adjudicationof patentability of patent applications, often lack thetime, resources and/or experience necessary to conduct adequateprior art search. This study aims to build an automatic andeffective system of patent citation recommendation for patentexaminers. In addition to focusing on content and bibliographicinformation, our proposed system considers another importantpiece of information that is known by patent examiners, namely, applicant citations. We integrate applicant citations and bibliographicinformation of patents into a heterogeneous citationbibliographicnetwork. Based on this network, we explore metapathsbased relationships between a query patent application anda candidate prior patent and classify them into two categories:(1) Bibliographic meta-paths, (2) Applicant Bibliographic metapaths. We propose a framework based on a two-phase rankingapproach: the first phase involves selection of a candidate subsetfrom the whole U. S. patent data, and the second phase usessupervised learning models to rank prior patents in the candidatesubset. The results show that both bibliographic informationand applicant citation information are very useful for examinercitation recommendation, and that our approach significantlyoutperforms a search engine.