Thanks　to　the　attention　mechanism,　self-attention　networks　(SANs)　have　been　widely　used　in　sequential　recommendation.　However,　most　existing　SANs　approaches　still　follow　an　old　fashion　generating　one　single　embedding　as　final　representation,　which　constraints　model＇s　capacity.　To　enrich　this　kind　of　representation,　sequential　recommender　uses　metadata　such　as　item　category　to　capture　user＇s　multi-interests.　But　this　method　will　not　reach　its　expectation　due　to　item＇s　long-tail　property.　This　property　will　result　a　large　constant　of　category　cannot　be　effectively　activated　by　the　lack　of　interaction　records.　Another　drawback　is　that　may　also　lead　to　over-parameterization　caused　by　the　massive　categories.　Particularly,　we　propose　a　Self-Attentive　Interest　Retrieval　network　(SAIR)　to　explore　a　context-aware　representation　from　user＇s　behaviors　while　not　fall　into　over-parameterization.　SAIR　works　in　a　typical　SANs　manner,　encode　the　behavior　sequence　using　self-attention,　and　we　propose　an　interest　retrieval　module　to　project　the　sequences　to　an　interest　relevance　distribution　adaptively.　And　we　leverage　an　interest-to-interest　interaction　to　generate　several　context-aware　interests　embeddings.　Then　we　fuse　multi-interest　embeddings　as　final　output.　Extensive　experiments　are　carried　out　on　three　real-world　datasets,　the　results　demonstrate　that　SAIR　outperforms　other　SANs　methods　and　other　state-of-the-art　algorithms　in　multiple　evaluation　metrics.　©　2022　IEEE.