  POI tagging aims to annotate a point of interest (POI) with some informative
tags, which facilitates many services related to POIs, including search,
recommendation, and so on. Most of the existing solutions neglect the
significance of POI images and seldom fuse the textual and visual features of
POIs, resulting in suboptimal tagging performance. In this paper, we propose a
novel Multi-Modal Model for POI Tagging, namely M3PT, which achieves enhanced
POI tagging through fusing the target POI's textual and visual features, and
the precise matching between the multi-modal representations. Specifically, we
first devise a domain-adaptive image encoder (DIE) to obtain the image
embeddings aligned to their gold tags' semantics. Then, in M3PT's text-image
fusion module (TIF), the textual and visual representations are fully fused
into the POIs' content embeddings for the subsequent matching. In addition, we
adopt a contrastive learning strategy to further bridge the gap between the
representations of different modalities. To evaluate the tagging models'
performance, we have constructed two high-quality POI tagging datasets from the
real-world business scenario of Ali Fliggy. Upon the datasets, we conducted the
extensive experiments to demonstrate our model's advantage over the baselines
of uni-modality and multi-modality, and verify the effectiveness of important
components in M3PT, including DIE, TIF and the contrastive learning strategy.
