Collaborative　filtering　usually　suffers　from　limited　performance　due　to　a　data　sparsity　problem.　Transfer　learning　presents　an　unprecedented　opportunity　to　alleviate　this　issue　through　the　transfer　of　useful　knowledge　from　an　auxiliary　domain　to　a　target　domain.　However,　the　situation　becomes　complicated　when　the　source　and　target　domain　share　partial　knowledge　with　each　other.　Transferring　the　unshared　part　across　domains　will　cause　negative　transfer　and　may　degrade　the　prediction　accuracy　in　the　target　domain.　To　address　this　issue,　in　this　paper,　we　present　a　novel　model　that　exploits　the　latent　factors　in　the　target　domain　against　the　negative　transfer.　First,　we　transfer　rating　patterns　from　the　source　domain　to　approximate　and　reconstruct　the　target　rating　matrix.　Second,　to　be　specific,　we　propose　a　partial-adaptation　nonnegative　matrix　factorization　method　to　correct　the　transfer　learning　result　and　restore　latent　factors　in　the　target.　The　final　experiments　completed　on　real　world　datasets　demonstrate　that　our　proposed　approach　effectively　addresses　the　negative　transfer　and　significantly　outperforms　the　state-of-art　transfer-learning　model.