Instagrammers, Fashionistas, and Me: Recurrent Fashion Recommendation with Implicit Visual Influence
Instagrammers, Fashionistas, and Me: Recurrent Fashion Recommendation with Implicit Visual Influence
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DOI：
10.1145/3357384.3358042
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Yin Zhang;James Caverlee
中科院分区：
文献类型：
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作者：
Yin Zhang;James Caverlee
Instagram、Facebook和其他社交媒体平台上关注时尚的关键观点博主正迅速成为关键的影响者。他们可以通过将高级时尚视觉演变与日常街头风格联系起来，激发消费者购买服装。在本文中，我们建立了第一个视觉影响力感知的时尚推荐（FIRN），利用时尚博客和他们的动态视觉帖子。具体来说，我们通过BiLSTM提取这些博主强调的动态时尚特征，该BiLSTM集成了大量的视觉帖子和社区影响力。然后，我们通过一个个性化的注意力层学习从博客到个人用户的隐式视觉影响漏斗。最后，我们将用户的个人风格和她喜欢的时尚功能，随着时间的推移，在一个经常性的推荐网络的动态时尚更新的服装推荐。实验表明，FIRN优于最先进的时尚博客，特别是对于那些最受时尚影响者影响的用户，与使用其他潜在的视觉信息源相比，利用时尚博客可以带来更大的推荐改进。我们还发布了时尚影响者的大时间感知高质量视觉数据集，可用于未来的研究。
Fashion-focused key opinion bloggers on Instagram, Facebook, and other social media platforms are fast becoming critical influencers. They can inspire consumer clothing purchases by linking high fashion visual evolution with daily street style. In this paper, we build thefirst visual influence-aware fashion recommender (FIRN) with leveraging fashion bloggers and their dynamic visual posts. Specifically, we extract thedynamic fashion features highlighted by these bloggers via a BiLSTM that integrates a large corpus of visual posts and community influence. We then learn theimplicit visual influence funnel from bloggers to individual users via a personalized attention layer. Finally, we incorporate user personal style and her preferred fashion features across time in a recurrent recommendation network for dynamic fashion-updated clothing recommendation. Experiments show that FIRN outperforms state-of-the-art fashion recommenders, especially for users who are most impacted by fashion influencers, and utilizing fashion bloggers can bring greater improvements in recommendation compared with using other potential sources of visual information. We also release a largetime-aware high-quality visual dataset of fashion influencers that can be exploited for future research.