| （英） |
Social media is a popular platform for brands to allocate marketing budget and build their relationship with customers nowadays. Posting images with a consistent concept on social media helps customers recognize, remember, and consider brands. This strategy is known as brand concept consistency in marketing literature. Consequently, brands spend immense manpower and financial resources in choosing which images to post or repost. Therefore, automatically recommending images with a consistent brand concept is a necessary task for social media marketing. In this study, we propose a content-based recommendation framework that learns the concept of brands and recommends images that are coherent with the brand. Specifically, brand representation is performed from the brand posts on social media. Existing methods rely on visual features extracted by pre-trained neural networks, which can represent objects in the image but not the style of the image. To bridge this gap, a framework using both object and style vectors as input is proposed to learn the brand representation. In addition, we show that the proposed method can not only be applied to brands but also be applied to influencers. The experimental results on two large-scale Instagram datasets show the superiority of the proposed method over state-of-the-art methods. |