Sequential Recommendation characterizes the evolving patterns by modelingitem sequences chronologically . We use thistransformer to generate fabricated historical items at the beginning of short sequences . We fine-tune the transformer using these augmentedsequences from the time order to predict the next item . Experiments on tworeal-world datasets verify the effectiveness of ASReP . The code is available on\url{https://://://www.g.com/DyGRec/ASRePĀ and the code can be found on the GitHub site of the project . Back to Mail Online home . Back To the page you came from .com/mailonline/newsquiz

Author(s) : Zhiwei Liu, Ziwei Fan, Yu Wang, Philip S. Yu

Links : PDF - Abstract

Code :
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Keywords : asrep - sequences - sequential - code - transformer -

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