Normalized Attraction Travel Personality Representation for Improving Travel Recommender Systems

Abstract

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.

Post Pandemic Tourism: Sentiment Analysis using Support Vector Machine Based on TikTok Data

“…Summary findings from sentiment analysis will aid tourists in selecting their tour destination and itinerary [6]. Tourists may do an information search to select the right location, which can be difficult due to the abundance of options and information on the Internet [7]. Sentiment analysis could be used to gather feedback for any tourist spots, thus helping people to choose the right vacations for them.…”