Who is the Mr. Right for your brand? - Discovering brand key assets via multi-modal asset-aware projection

Following the rising prominence of online social networks, we observe an emerging trend for brands to adopt influencer marketing, embracing key opinion leaders (KOLs) to reach potential customers (PCs) online. Owing to the growing strategic importance of these brand key assets, this paper presents a novel feature extraction method named Multi-modal Asset-aware Projection (M2A2P) to learn a discriminative subspace from the high-dimensional multi-modal social media data for effective brand key asset discovery. By formulating a new asset-aware discriminative information preserving criterion, M2A2P differentiates with the existing multi-model feature extraction algorithms in two pivotal aspects: 1) We consider brand's highly imbalanced class interest steering towards the KOLs and PCs over the irrelevant users; 2) We consider a common observation that a user is not exclusive to a single class (e.g. a KOL can also be a PC). Experiments on a real-world apparel brand key asset dataset validate the effectiveness of the proposed method.