Collaborative　filtering　techniques　are　a　common　approach　for　building　recommendations,　and　have　been　widely　applied　in　real　recommender　systems.　However,　collaborative　filtering　usually　suffers　from　limited　performance　due　to　the　sparsity　of　user-item　interaction.　To　address　this　issue,　auxiliary　information　is　usually　used　to　improve　the　performance.　Transfer　learning　provides　the　key　idea　of　using　knowledge　from　auxiliary　domains.　An　assumption　of　transfer　learning　in　collaborative　filtering　is　that　the　source　domain　is　a　full　rating　matrix,　which　may　not　hold　in　many　real-world　applications.　In　this　paper,　we　investigate　how　to　leverage　rating　patterns　from　multiple　incomplete　source　domains　to　improve　the　quality　of　recommender　systems.　First,　by　exploiting　the　transferred　learning,　we　compress　the　knowledge　from　the　source　domain　into　a　cluster-level　rating　matrix.　The　rating　patterns　in　the　low-level　matrix　can　be　transferred　to　the　target　domain.　Specifically,　we　design　a　knowledge　extraction　method　to　enrich　rating　patterns　by　relaxing　the　full　rating　restriction　on　the　source　domain.　Finally,　we　propose　a　robust　multiple-rating-pattern　transfer　learning　model　for　cross-domain　collaborative　filtering,　which　is　called　MINDTL,　to　accurately　predict　missing　values　in　the　target　domain.　Extensive　experiments　on　real-world　datasets　demonstrate　that　our　proposed　approach　is　effective　and　outperforms　several　alternative　methods.