The　development　of　Internet　comes　up　with　the　prosperity　of　E-commerce　all　over　the　world.　In　order　to　promote　sales　and　save　consumers＇　labor　in　commodity　browsing,　recommender　systems　are　proposed　by　E-commerce　platforms　to　provide　online　consumers　with　products　and　services　of　their　potential　interests.　The　primary　challenge　in　recommendation　roots　in　the　intricacy　in　quantifying　users＇　preferences　on　items　with　the　reality　of　data　sparsity　and　the　computation　complexity.　Hence,　more　and　more　researchers　are　attempting　deep　learning　techniques　to　deal　with　the　challenge　with　the　hope　of　using　advanced　algorithms　to　alleviate　the　intricacy.　Word　embedding　is　used　to　learn　the　association　of　items　in　a　space　of　low　dimensionality.　Multi-layer　perception　is　used　to　learn　users＇　preferences　on　items　in　a　data-driven　manner　with　a　customized　loss　function.　The　future　work　of　recommender　systems　includes　three　folds.　The　one　is　to　make　use　of　multi-source　data　to　combine　implicit　and　explicit　user　behavior　data　to　address　the　problem　of　data　sparsity.　The　second　is　dynamic　recommendation　with　the　changing　users＇　preferences　on　items　and　make　recommender　systems　light-weight　and　useable　in　complex　scenarios.　The　third　is　to　provide　effective　and　verifiable　recommendation　under　the　premise　of　user　privacy　protection