Recommender　systems　aim　to　provide　users　with　preferred　items　to　address　the　information　overload　problem　in　the　Web　era.　Social　relations,　item　connections,　and　user-generated　item　reviews　and　ratings　play　important　roles　in　recommender　systems　as　they　contain　abundant　potential　information.　Many　methods　have　been　proposed　to　predict　users＇　ratings　by　learning　latent　topic　factors　from　their　reviews　and　ratings　of　corresponding　items.　However,　these　methods　ignore　the　relationships　among　items　and　cannot　make　full　use　of　the　complicated　relations　between　reviews　and　ratings.　Motivated　by　this　observation,　we　integrate　ratings,　reviews,　user　connections　and　item　relations　to　improve　recommendations　by　combining　matrix　factorization　with　the　Latent　Dirichlet　Allocation　(LDA)　model.　Experimental　results　on　two　real-world　datasets　prove　that　item-item　relations　contain　useful　information　for　recommendations,　and　our　model　effectively　improves　recommendation　quality.　©　2018　-　IOS　Press　and　the　authors.　All　rights　reserved.