Social　recommendation　aims　to　improve　the　recommendation　performance　by　learning　user　interest　and　social　representations　from　users＇　interaction　records　and　social　relations.　Intuitively,　these　learned　representations　entangle　user　interest　factors　with　social　factors　because　users＇　interaction　behaviors　and　social　relations　affect　each　other.　A　high-quality　recommender　system　should　provide　items　to　a　user　according　to　his/her　interest　factors.　However,　most　existing　social　recommendation　models　aggregate　the　two　kinds　of　representations　indiscriminately,　and　this　kind　of　aggregation　limits　their　recommendation　performance.　In　this　paper,　we　develop　a　model　called　Disentangled　Variational　autoencoder　for　Social　Recommendation　(DVSR)　to　disentangle　interest　and　social　factors　from　the　two　kinds　of　user　representations.　Firstly,　we　perform　a　preliminary　analysis　of　the　entangled　information　on　three　popular　social　recommendation　datasets.　Then,　we　present　the　model　architecture　of　DVSR,　which　is　based　on　the　Variational　AutoEncoder　(VAE)　framework.　Besides　the　traditional　method　of　training　VAE,　we　also　use　contrastive　estimation　to　penalize　the　mutual　information　between　interest　and　social　factors.　Extensive　experiments　are　conducted　on　three　benchmark　datasets　to　evaluate　the　effectiveness　of　our　model.