Web　Intelligence　(WI)　is　a　new　and　active　research　field　in　current　AI　and　IT.　Personalized　recommendation　in　an　intelligent　B2C　portal　is　an　important　research　topic　in　WI.　In　this　paper,　we　first　investigate　the　architecture　of　a　B2C　portal　from　the　viewpoint　of　conceptual　levels　of　WI.　Aiming　at　data　mining　of　knowledge-level　in　a　B2C　portal,　we　present　a　new　improved　learning　algorithm　of　Bayesian　Networks,　which　consists　of　two　major　contributions,　namely,　making　the　best　of　lower　order　Conditional　Independence　(CI)　tests　and　accelerating　search　process　by　means　of　sort　order　for　parent　nodes.　By　a　number　of　experiments　on　ALARM　datasets,　we　find　that　the　proposed　algorithm　is　both　more　efficient　and　effective　than　others.　We　have　applied　this　algorithm　to　a　commodity　recommendation　system　in　a　B2C　portal.　Our　experimental　results　demonstrate　that　the　recommendation　method　based　on　a　Customer　Shopping　Model　(CSM)　produced　by　the　new　algorithm　outperforms　some　traditional　ones　in　rates　of　coverage　and　precision.　©　2004　IEEE.