知识增强的神经时尚趋势预测
Knowledge Enhanced Neural Fashion Trend Forecasting
摘要：
流行趋势预测是学术界和工业界的一项重要任务。虽然有人致力于解决这个具有挑战性的任务，但他们只研究了有限的具有高度季节性或简单图案的时尚元素，这很难揭示真正的时尚趋势。为了更有洞察力的时尚趋势预测，这项工作的重点是调查细粒度的时尚元素的趋势，为特定的用户组。我们首先贡献了一个从Instagram收集的大规模时尚趋势数据集（FIT），其中包含提取的时间序列时尚元素记录和用户信息。此外，为了有效地对具有相当复杂模式的时尚元素的时间序列数据进行建模，我们提出了一种知识增强的递归网络模型（KERN），该模型利用了深度递归神经网络对时间序列数据建模的能力。此外，它利用内部和外部的知识在时尚领域，影响时尚元素趋势的时间序列模式。这种领域知识的结合进一步增强了深度学习模型在捕捉特定时尚元素的模式和预测未来趋势方面的能力。大量的实验表明，所提出的KERN模型可以有效地捕捉客观时尚元素的复杂模式，从而做出更好的时尚趋势预测。
英文摘要：
Fashion trend forecasting is a crucial task for both academia and industry. Although some efforts have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real fashion trends. Towards insightful fashion trend forecasting, this work focuses on investigating fine-grained fashion element trends for specific user groups. We first contribute a large-scale fashion trend dataset (FIT) collected from Instagram with extracted time series fashion element records and user information. Further-more, to effectively model the time series data of fashion elements with rather complex patterns, we propose a Knowledge EnhancedRecurrent Network model (KERN) which takes advantage of the capability of deep recurrent neural networks in modeling time-series data. Moreover, it leverages internal and external knowledge in fashion domain that affects the time-series patterns of fashion element trends. Such incorporation of domain knowledge further enhances the deep learning model in capturing the patterns of specific fashion elements and predicting the future trends. Extensive experiments demonstrate that the proposed KERN model can effectively capture the complicated patterns of objective fashion elements, therefore making preferable fashion trend forecast.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
