SBTRec- A Transformer Framework for Personalized Tour Recommendation Problem with Sentiment Analysis

TL;DR

This paper introduces SBTRec, a BERT-based trajectory recommendation system that incorporates sentiment analysis to personalize tourist itineraries, outperforming existing methods in accuracy across multiple city datasets.

Contribution

The paper presents a novel SBTRec algorithm that combines POI check-in data with sentiment analysis to improve personalized tour recommendations without requiring city-specific modifications.

Findings

SBTRec achieves an average F1 score of 61.45%.

Outperforms baseline sequence prediction algorithms.

Flexible and adaptable to different cities and scenarios.

Abstract

When traveling to an unfamiliar city for holidays, tourists often rely on guidebooks, travel websites, or recommendation systems to plan their daily itineraries and explore popular points of interest (POIs). However, these approaches may lack optimization in terms of time feasibility, localities, and user preferences. In this paper, we propose the SBTRec algorithm: a BERT-based Trajectory Recommendation with sentiment analysis, for recommending personalized sequences of POIs as itineraries. The key contributions of this work include analyzing users' check-ins and uploaded photos to understand the relationship between POI visits and distance. We introduce SBTRec, which encompasses sentiment analysis to improve recommendation accuracy by understanding users' preferences and satisfaction levels from reviews and comments about different POIs. Our proposed algorithms are evaluated against other sequence prediction methods using datasets from 8 cities. The results demonstrate that SBTRec achieves an average F1 score of 61.45%, outperforming baseline algorithms. The paper further discusses the flexibility of the SBTRec algorithm, its ability to adapt to different scenarios and cities without modification, and its potential for extension by incorporating additional information for more reliable predictions. Overall, SBTRec provides personalized and relevant POI recommendations, enhancing tourists' overall trip experiences. Future work includes fine-tuning personalized embeddings for users, with evaluation of users' comments on POIs,~to further enhance prediction accuracy.