{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/behavior-sequence-transformer-for-e-commerce","title":"Behavior Sequence Transformer for E-commerce Recommendation in Alibaba","arxiv_id":"1905.06874","date":"2019-05-15","proceeding":null,"authors":["Qiwei Chen","Huan Zhao","Wei Li","Pipei Huang","Wenwu Ou"],"abstract":"Deep learning based methods have been widely used in industrial recommendation systems (RSs). Previous works adopt an Embedding&MLP paradigm: raw features are embedded into low-dimensional vectors, which are then fed on to MLP for final recommendations. 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