Dual Preference Perception Network for Fashion Recommendation in Social Internet of Things

Nowadays, with the continuous development of information technology, the application scenarios of the Internet of Things (IoT) are progressively expanding to the social field, engendering widespread attention to the Social IoT (SIoT). Personalized fashion recommendation that possesses the potential to establish social relationships between clothing and humans has substantially broadened the scope of the SIoT, particularly with the flourishing fashion industry and the ascent of smart home. Compared to conventional recommendations, fashion recommendation generally suggests a collection of items rather than individual pieces for users. Additionally, considering the public acceptance alongside the user-specific preference is reasonable for fashion recommendation, however, current methods often overlook the former. To comprehensively capture the public acceptance and the user-specific preference, we propose a dual preference perception network (DP2Net) for fashion recommendation. First, a fashion corpus is constructed to facilitate the condensation of general taste, wherein adversarial learning and determinantal point process are leveraged to ensure representativeness and diversity of the corpus. Second, a user-general preference perception module is built based on a bottleneck transformer structure to generate aggregated representations for the corpus. Third, a user-specific preference perception module is constructed to acquire collaborative representations of users and outfits by employing an attentive heterogeneous graph embedding. The final loss functions of two preference perception modules are constructed by combining the representations of users, outfits, and the corpus. Experiments on large-scale real-world data sets demonstrate the effectiveness of the proposed method. To facilitate reproducible research, we have made our code publicly available at https://github.com/KaiZhang1228/DP2Net .