Collaborative　filtering　(CF)　has　been　generally　used　in　recommender　systems　when　faced　some　practical　problems.　Due　to　the　sparsity　of　the　rating　matrix,　the　traditional　CF-based　approach　has　a　significant　decline　in　recommendation　performance.　Gradually,　a　hybrid　method,　using　side　information　and　rating　information,　has　been　widely　employed　and　achieves　great　performance.　Together　with　side　information　and　rating　information,　the　hybrid　method　can　overcome　the　data　sparsity　and　cold-start　problems.　However,　they　seem　to　fail　to　take　into　consideration　the　fact　that　the　sparsity　of　single　side　information.　To　solve　this　problem,　we　take　full　advantage　of　the　characteristics　of　deep　learning　that　can　learn　effective　representation　and　propose　a　novel　deep　learning　model　named　additional　variational　autoencoder　that　considers　both　content　and　tag　information　of　the　item.　The　model　learns　effective　latent　representations　from　additional　side　information,　including　content　information　and　tag　information　in　an　unsupervised　manner.　With　the　help　of　graphical　models,　it　can　extract　the　implicit　relationships　between　users　and　items　effectively.　A　large　number　of　experimental　results　on　two　actual　datasets　show　that　our　proposed　model　is　superior　to　other　methods,　and　the　performance　improvement　is　achieved.