Two major challenges in collaborative filtering are the efficiency of the algorithms and the quality of the recommendations. A variety of machine learning methods have been applied to address these two issues, including feature selection, instance selection, and clustering. Most existing methods either compromise computational complexity or prediction precision. Two novel, scalable memory-based CF algorithms are proposed, namely BS1, BS2, which combine the strengths of existing techniques while discarding their weaknesses. Experiments show that both the efficiency and performance have been improved when compared to three classical techniques: VSIM, FCBF and PD.