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Computer Science > Multimedia

arXiv:2109.08275 (cs)
[Submitted on 17 Sep 2021 (v1), last revised 28 Jan 2023 (this version, v2)]

Title:Multi-Level Visual Similarity Based Personalized Tourist Attraction Recommendation Using Geo-Tagged Photos

Authors:Ling Chen, Dandan Lyu, Shanshan Yu, Gencai Chen
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Abstract: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.
Comments: Accepted by TKDD
Subjects: Multimedia (cs.MM); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2109.08275 [cs.MM]
  (or arXiv:2109.08275v2 [cs.MM] for this version)
  https://doi.org/10.48550/arXiv.2109.08275

Submission history

From: Ling Chen [view email]
[v1] Fri, 17 Sep 2021 01:34:15 UTC (356 KB)
[v2] Sat, 28 Jan 2023 02:56:58 UTC (1,222 KB)
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