Weighted multi-information constrained matrix factorization for personalized travel location recommendation based on geo-tagged photos

Given travel history, travel location recommendation can automatically suggest users where to visit. Huge efforts have been devoted to introducing different additional information (e.g., sequential, textual, geographical, and visual information) for enhancing recommendation performance. However, existing methods only consider limited additional information and treat different information equally. In this paper, we present W eighted Mult i -I n formation Constraine d M atrix F actorization (WIND-MF) for personalized travel location recommendation based on geo-tagged photos. On one hand, photos (visual information), users’ visit sequences (sequential information), and textual tags (textual information) are leveraged to comprehensively profile users and travel locations. On the other hand, visual, sequential, and textual similarities as well as geographical distance based co-visit probabilities are assigned with different weights to constrain the factorization of the original user-travel location matrix. We experimented on a dataset of six cities in China, and the experiment results verify the superiority of the proposed method. The code and dataset is available at https://github.com/revaludo/WIND-MF .