Personalized Travel Recommendation Based on the Fusion of TGI and POI Algorithms

Abstract

On the way to travel, the public expect to get a tourism experience with low cost, convenient travel, and high comfort. At the same time, they also have different tourism needs such as history and culture, natural landscape, and food shopping. To address the problem that traditional travel route recommendation algorithms have limited accuracy and only analyze text or pictures alone, we propose a personalized travel route recommendation algorithm that integrates text and photo information from travelogues and obtain the historical tourism footprint of tourists by analyzing travel notes. According to the frequency and cooccurrence of scenic spots in the travel notes and the number of photos taken by each scenic spot, the popularity of scenic spots and the interest preferences of various types of tourists are analyzed. Under the given starting and ending points or passing points, the optimal tourism route generation method is designed. Experiments on the real data set of Ctrip Travel website show that the recommendation accuracy of this algorithm is significantly improved compared with the traditional algorithm which only uses travel notes text or photos. Compared with the algorithm that only considers the popularity of interest points or tourists’ interest preferences, the accuracy of the route recommended by the algorithm is improved. Compared with the algorithm that only considers the cooccurrence of scenic spots or only considers the influence of photos, this algorithm can obtain a better popularity score of scenic spots. This method integrates the two kinds of information including picture and text, fully considering the interest of users with high practicability.

Personalized Tourism Recommendations and the E-Tourism User Experience

“…However, the current big data and AI literature has not been systematically explored in depth (Samara et al, 2020). Conversely, the literature on PTRs is almost exclusively based on technical aspects of research (Angskun & Angskun, 2018; Chang et al, 2022; Fan & Zhang, 2022), seldom evaluating tourists (Shi et al, 2021). Taken together, research investigating the impact of big data and AI-based PTRs in the context of smart tourism has been sparse.…”

“…Personalized Tourism Recommendations (PTR). Collating the existing literature, we have categorized personalized tourism recommendations into six types: (1) personalized attraction recommendations, which provide a rated list of attractions (Angskun & Angskun, 2018); (2) personalized travel route recommendations, which combine user behavior habits, interest preferences, and route popularity (Fan & Zhang, 2022); (3) comprehensive user-adapted travel planning recommendations, which integrate travel schedules, tourist attractions visited, local hotel selection, and travel budget calculation (Chiang & Huang, 2015); (4) personalized hotel recommendations, which combine user characteristics and their personalized preferences with public preferences (K. Chen, Wang, & Zhang, 2021);…”

“…Yu et al, 2023;Q. Zhang et al, 2021) and is a leading Chinese integrated etourism platform that has made many attempts to develop personalized tourism recommendations using big data and AI (K. Chen, Wang, & Zhang, 2021;Fan & Zhang, 2022). PTRs and algorithms are indispensable to discovering suitable travel products and services for users, tapping potential interests, and minimizing information overload.…”

“…While AI has been around for 60 years and is not a new technology, the explosive development of big data technologies has injected fresh power into artificial intelligence (Duan et al, 2019). The enhanced computer storage and extremely quick data processing devices associated with big data technology, combined with AI technology’s pattern recognition and text mining, provide the technical conditions for decision-making systems such as PTRs ( Duan et al, 2019; Fan & Zhang, 2022). This section reviews research related to big data and AI and their applications in the tourism industry.…”

“…Collating the existing literature, we have categorized personalized tourism recommendations into six types: (1) personalized attraction recommendations, which provide a rated list of attractions (Angskun & Angskun, 2018); (2) personalized travel route recommendations, which combine user behavior habits, interest preferences, and route popularity (Fan & Zhang, 2022); (3) comprehensive user-adapted travel planning recommendations, which integrate travel schedules, tourist attractions visited, local hotel selection, and travel budget calculation (Chiang & Huang, 2015); (4) personalized hotel recommendations, which combine user characteristics and their personalized preferences with public preferences (K. Chen, Wang, & Zhang, 2021); (5) personalized online booking recommendations, which integrate the identification of online opinion experts, construction of social networks, detection of user communities, and interaction of PTRs (Chang et al, 2022); and (6) personalized travel package recommendations, which produce dynamic suggestions for users based on geo-tagged photos, taking into consideration multidimensional data such as time, location, implicit ratings, tourist characteristics, and their sequential travel patterns (Kolahkaj et al, 2020).…”