In　the　past　decade,　recommender　systems　have　been　widely　used　to　provide　users　with　personalized　products　and　services.　However,　most　traditional　recommender　systems　are　still　facing　a　challenge　in　dealing　with　the　huge　volume,　complexity,　and　dynamics　of　information.　To　tackle　this　challenge,　many　studies　have　been　conducted　to　improve　recommender　system　by　integrating　deep　learning　techniques.　As　an　unsupervised　deep　learning　method,　autoencoder　has　been　widely　used　for　its　excellent　performance　in　data　dimensionality　reduction,　feature　extraction,　and　data　reconstruction.　Meanwhile,　recent　researches　have　shown　the　high　efficiency　of　autoencoder　in　information　retrieval　and　recommendation　tasks.　Applying　autoencoder　on　recommender　systems　would　improve　the　quality　of　recommendations　due　to　its　better　understanding　of　users＇　demands　and　characteristics　of　items.　This　paper　reviews　the　recent　researches　on　autoencoder-based　recommender　systems.　The　differences　between　autoencoder-based　recommender　systems　and　traditional　recommender　systems　are　presented　in　this　paper.　At　last,　some　potential　research　directions　of　autoencoder-based　recommender　systems　are　discussed.