  Fashion, and especially apparel, is the fastest-growing category in online
shopping. As consumers requires sensory experience especially for apparel goods
for which their appearance matters most, images play a key role not only in
conveying crucial information that is hard to express in text, but also in
affecting consumer's attitude and emotion towards the product. However,
research related to e-commerce product image has mostly focused on quality at
perceptual level, but not the quality of content, and the way of presenting.
This study aims to address the effectiveness of types of image in showcasing
fashion apparel in terms of its attractiveness, i.e. the ability to draw
consumer's attention, interest, and in return their engagement. We apply
advanced vision technique to quantize attractiveness using three common display
types in fashion filed, i.e. human model, mannequin, and flat. We perform
two-stage study by starting with large scale behavior data from real online
market, then moving to well designed user experiment to further deepen our
understandings on consumer's reasoning logic behind the action. We propose a
Fisher noncentral hypergeometric distribution based user choice model to
quantitatively evaluate user's preference. Further, we investigate the
potentials to leverage visual impact for a better search that caters to user's
preference. A visual attractiveness based re-ranking model that incorporates
both presentation efficacy and user preference is proposed. We show
quantitative improvement by promoting visual attractiveness into search on top
of relevance.
