The　personalized　video　recommendation　system　provides　users　with　great　convenience　while　surfing　in　the　video　websites.　Among　many　algorithms　adopted　by　recommendation　system,　the　collaborative　filtering　algorithm　is　the　most　widely　used　and　has　achieved　great　success　in　practical　applications,　however,　the　recommended　performance　suffers　from　the　problem　of　data　sparsity　severely.　We　propose　a　model　that　adopts　Doc2Vec　to　deal　with　video＇s　text　information　and　integrates　genre　information　into　rating　matrix　pre-padding　to　reduce　the　sparsity　of　ratings.　The　experimental　results　show　that　pre-padding　ratings　is　of　high　quality　and　the　algorithms　based　on　collaborative　filtering　achieve　better　performance　on　the　padded　datasets.