A novel Sequence-Aware personalized recommendation system based on multidimensional information

Due to the rapid growth of the information overload issue, recommender systems have become necessary and are implemented in numerous facets of human life, including the tourism industry. Today, technological advancements have significantly altered our travels, and these advancements promise even more interactive and exciting experiences in the future. Nowadays, planning and arranging a customized trip well in advance is ideal, as the process can be challenging and time-consuming. This paper proposes a hybrid approach that leverages multidimensional data to enhance personalized trip recommendations while addressing several shortcomings of existing recommender systems, such as their inability to account for dynamic user preferences and diverse contexts. To this end, the proposed method, considering the data sparsity issue, employs a clustering algorithm to reduce the time complexity of discovering Points of Interest (POIs). This approach utilizes user demographic information to address the cold start issue while improving the collaborative filtering (CF) paradigm through an asymmetric schema. Furthermore, this study exploits the context vector model and the Term-Frequency-Inverse-Document-Frequency (TF-IDF) algorithm to present a novel method for quantifying context similarity. Moreover, the method retrieves and ranks a list of candidate routes optimized by applying personalized POIs to sequential travel patterns. Finally, the experimental results demonstrate the superiority of the approach in terms of Precision, Recall, RMSE, MAP, F-Score, and nDCG compared to previous works based on Flickr and STS datasets.