Recommender　system　has　been　recognizedas　a　superior　way　for　solving　personal　information　overload　problem.More　and　more　aspect-based　models　are　leveraging　user　ratings　and　extracting　information　from　review　texts　to　support　recommendation.Aspect-based　latent　factor　model　predicts　user　ratings　relying　on　latent　aspect　inferred　from　user　reviews.It　usually　constructs　only　a　single　global　model　for　all　users,which　may　be　not　sufficient　to　capture　the　diversity　of　users＇　preferences　and　leave　some　items　or　users　be　badly　modeled.We　propose　a　Hybrid　aspect-based　latent　factor　model　(HALFM),which　jointly　optimizes　the　Global　aspect-based　latent　factor　model　(GALFM)　and　the　Local　Aspect-based　Latent　Factor　Models　(LALFM),their　user-specific　combination,and　the　assignment　of　users　to　the　LALFMs.HALFM　makes　prediction　by　combining　user-specific　of　GALFM　and　many　LALFMs.Experimental　results　demonstrate　that　the　proposed　HALFM　outperforms　most　of　aspect-based　recommendation　techniques　in　rating　prediction.