Geo-tagged photo based tourist attraction recommendation can discover users'
travel preferences from their taken photos, so as to recommend suitable tourist
attractions to them. However, existing visual content based methods cannot
fully exploit the user and tourist attraction information of photos to extract
visual features, and do not differentiate the significances of different
photos. In this paper, we propose multi-level visual similarity based
personalized tourist attraction recommendation using geo-tagged photos (MEAL).
MEAL utilizes the visual contents of photos and interaction behavior data to
obtain the final embeddings of users and tourist attractions, which are then
used to predict the visit probabilities. Specifically, by crossing the user and
tourist attraction information of photos, we define four visual similarity
levels and introduce a corresponding quintuplet loss to embed the visual
contents of photos. In addition, to capture the significances of different
photos, we exploit the self-attention mechanism to obtain the visual
representations of users and tourist attractions. We conducted experiments on a
dataset crawled from Flickr, and the experimental results proved the advantage
of this method.

提出了一种基于多级视觉相似性和自关注机制的个性化旅游景点推荐模型 (MEAL)，该模型利用旅客拍摄的照片和用户的交互行为数据来获取最终的用户和旅游景点嵌入向量，并以此为基础预测旅游景点的访问概率。