Evolutionary Product Description Generation: A Dynamic Fine-Tuning Approach Leveraging User Click Behavior

SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval Virtual Event China July, 2020, pp. 119-128, 2020.

Cited by: 0|Bibtex|Views88|DOI:https://doi.org/10.1145/3397271.3401140
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Abstract:

Conventional models on Neural Text Generation (NTG) determine the output distribution by applying maximum likelihood estimation on training corpora. However, as user preference for generated content can be constantly changing, an optimized text generator needs to assimilate such non-static nature into the outcome adaptively. In this paper...More

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