Personalized Recommendation Method for Cultural Creative Products in Tourism Cities Based on User Profiles
Jin Tian Huang · Procedia Computer Science · 2024
This examine introduces a novel personalised recommendation technique for cultural and innovative merchandise in traveler cities, leveraging the superior abilities of BERT4Rec along designated user photos to noticeably enhance the advice machine's precision and customization. with the aid of undertaking an in-intensity evaluation of users' historical conduct, the studies harnesses the state-of-the-art deep mastering and self-attention mechanisms intrinsic to BERT4Rec. This technique adeptly captures the nuanced long-range dependencies in user activities, imparting a profound perception into the styles and selections that define user behavior. The innovation extends to integrating these dynamic behavioral insights with the static demographic statistics of users—such as age, gender, and occupation—to forge a extra nuanced and whole user profile. This holistic view not solely mirrors the users' beyond interactions however also weaves in fundamental demographic information, paving the way for tailored and nuanced suggestions. The deployment of BERT4Rec stands as a testomony to its prowess in decoding complex consumer engagement tendencies and tendencies, whilst the fusion with consumer pix extensively bolsters the system's potential to cater to the various and specific desires of users.Empirical proof underscores the prevalence of this technique over modern tourism product advice algorithms, marking a giant stride in the realm of cultural innovative product recommendations inside traveler cities. furthermore, this paper's insights and methodologies provide treasured implications for reinforcing advice structures across severa domain names.