Travel recommender systems (TRSs) aim to reduce travel-related search overload. A significant part of a TRS is representing attractions in a way that reflect the explicit and implicit features of attractions. However, traditional attraction representation methods may not provide a complete image of attractions. Building on the notions of user travel styles (UTSs) and the wisdom of crowds, we propose a method derived from topic-model-based models to represent travel attractions, called the Normalized Attraction Travel Personality (NATP) representation. This approach attempts to leverage the semantics of attraction reviews to model user travel personalities (UTPs), which collectively can construct the attraction travel personality (ATP) representation. Furthermore, we regularize and normalize the ATP representation to obtain our proposed representation. This NATP-based attraction representation could capture implicit characteristics of attractions revealed by the wisdom of crowds. Our experiments show that our representation method gained better results when evaluated against comparative approaches in terms of rating prediction and recommendation ranking quality, indicating the effectiveness of the proposed attraction representation. Lastly, we qualitatively investigate how our attraction representation surpasses the state-ofthe-art representation methods.INDEX TERMS Content-based filtering, attraction representation, knowledge discovery, travel styles, travel recommender systems.