A personalized context and sequence aware point of interest recommendation

This study introduces an innovative hybrid approach for personalized trip recommendations, aiming to enhance existing recommender systems by leveraging multidimensional data. Our proposed method integrates user preferences and diverse contextual factors to address challenges related to data sparsity effectively. To overcome this hurdle, our methodology employs a clustering approach, streamlining the extraction of Points of Interest (PoI) and reducing computational complexity. The framework comprises three key components: I) a unique strategy for context assessment, achieved by combining contextual information in vector form through the Term-Frequency-Inverse-Document-Frequency technique, II) the incorporation of tourist demographic information to alleviate the Cold Start problem, and III) the implementation of an asymmetric schema that elevates the traditional similarity paradigm. Moreover, our approach utilizes personalized PoIs in consecutive travel patterns, enabling the retrieval and ranking of an optimal list of potential routes. The experimental results based on Flickr and Yelp datasets reveal that the proposed method surpasses prior work on all three metrics, achieving a significant 8% increase in precision and an 11% increase in F-Score, thereby enhancing the quality metrics of personalized trip recommendations.