With　the　increasing　number　of　scientific　papers,　researchers　find　it　more　and　more　difficult　to　obtain　relevant　and　appropriate　papers　to　cite.　Citation　recommendation　aims　to　overcome　this　problem　by　providing　a　reference　paper　list　for　a　given　manuscript.　In　this　paper,　we　propose　a　bibliographic　network　representation　(BNR)　model,　which　simultaneously　incorporates　bibliographic　network　structure　and　content　of　different　kinds　of　objects　(authors,　papers,　and　venues)　for　efficient　recommendation.　The　proposed　model　also　makes　personalized　citation　recommendation　possible,　which　is　a　new　issue　that　a　few　papers　addressed　in　the　past.　When　conducting　experiments　on　the　ACL　Anthology　Network　and　DBLP　datasets,　the　results　demonstrate　that　the　proposed　BNR-based　citation　recommendation　approach　is　able　to　achieve　considerable　improvement　over　other　network　representation-based　citation　recommendation　approaches.　The　performance　of　the　personalized　recommendation　approach　is　also　competitive　with　the　non-personalized　recommendation　approach.