Recommendation　methods　improve　rating　prediction　performance　by　learning　selection　bias　phenomenon　-users　tend　to　rate　items　they　like.　These　methods　model　selection　bias　by　calculating　the　propensities　of　ratings,　but　inaccurate　propensity　could　introduce　more　noise,　fail　to　model　selection　bias,　and　reduce　prediction　performance.　We　argue　that　learning　interaction　features　can　effectively　model　selection　bias　and　improve　model　performance,　as　interaction　features　explain　the　reason　of　the　trend.　Reviews　can　be　used　to　model　interaction　features　because　they　have　a　strong　intrinsic　correlation　with　user　interests　and　item　interactions.　In　this　study,　we　propose　a　preference-　and　bias　-oriented　fusion　learning　model　(PBFL)　that　models　the　interaction　features　based　on　reviews　and　user　preferences　to　make　rating　predictions.　Our　proposal　both　embeds　traditional　user　preferences　in　reviews,　interactions,　and　ratings　and　considers　word　distribution　bias　and　review　quoting　to　model　interaction　features.　Six　realworld　datasets　are　used　to　demonstrate　effectiveness　and　performance.　PBFL　achieves　an　average　improvement　of　4.46%　in　root　-mean　-square　error　(RMSE)　and　3.86%　in　mean　absolute　error　(MAE)　over　the　best　baseline.