With　the　explosive　growth　of　mobile　videos,　helping　users　quickly　and　effectively　find　mobile　videos　of　interest　and　further　provide　personalized　recommendation　services　are　the　developing　trends　of　mobile　video　applications.　Mobile　videos　are　characterized　by　their　wide　variety,　single　content,　and　short　duration,　and　thus　traditional　personalized　video　recommendation　methods　cannot　produce　effective　recommendation　performance.　Therefore,　a　personalized　mobile　video　recommendation　method　is　proposed　based　on　user　preference　modeling　by　deep　features　and　social　tags.　The　main　contribution　of　our　work　is　three-fold:　(1)　deep　features　of　mobile　videos　are　extracted　by　an　improved　exponential　linear　units-3D　convolutional　neural　network　(ELU-3DCNN)　for　representing　video　content;　(2)　user　preference　is　modeled　by　combining　user　preference　for　deep　features　with　user　preference　for　social　tags　that　are　respectively　modeled　by　maximum　likelihood　estimation　and　exponential　moving　average　method;　(3)　a　personalized　mobile　video　recommendation　system　based　on　user　preference　modeling　is　built　after　detecting　key　frames　with　a　differential　evolution　optimization　algorithm.　Experiments　on　YouTube-8M　dataset　have　shown　that　our　method　outperforms　state-of-the-art　methods　in　terms　of　both　precision　and　recall　of　personalized　mobile　video　recommendation.