Recommender　systems　aim　to　provide　users　with　preferred　items　to　tackle　the　information　overload　problem　in　the　Web　era.　Social　relations,　item　connections,　and　user　generated　reviews　on　items　contain　abundant　potential　information.　By　combining　matrix　factorization　with　latent　Dirichlet　allocation,　we　integrate　ratings,　reviews,　user　similarity　and　item　similarity　in　recommender　systems.　The　experimental　result　on　a　real　-world　dataset　proves　that　both　item　connection　and　user　connection　contain　useful　sources　for　recommendation,　and　our　model　can　effectively　improve　recommendation　quality.