Unified Visual Preference Learning for User Intent Understanding
Yihua Wen, Si Chen, Yu Tian, Wanxian Guan, Pengjie Wang, Hongbo Deng, Jian Xu, Bo Zheng, Zihao Li, Lixin Zou, Chenliang Li · 2024
In the world of E-Commerce, the core task is to understand the personalized preference from various kinds of heterogeneous information, such as textual reviews, item images and historical behaviors. In current systems, these heterogeneous information are mainly exploited to generate better item or user representations. For example, in scenario of visual search, the importance of modeling query image has been widely acknowledged. But, these existing solutions focus on improving the representation quality of the query image, overlooking the personalized visual preference of the user. Note that the visual features affect the user's decision significantly, e.g., the user could be more likely to click the items with her preferred design. Hence, it is fruitful to exploit the visual preference to deliver better capacity for personalization.