Yin Zhang
ABSTRACT
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.
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Index Terms
- Instagrammers, Fashionistas, and Me: Recurrent Fashion Recommendation with Implicit Visual Influence
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