Cultural tourism attraction recommendation model based on optimized weighted association rule algorithm

To address the challenge of users selecting rich tourism resources, this study proposes a model for cultural tourism attraction recommendation using an optimized weighted association rule algorithm. This model includes time and season weight for tourist attraction recommendations. This model proposes improvement methods to address some inherent issues in traditional tourism recommendation models. Firstly, it constructed a recommendation model for cultural tourism attractions, and then optimized the weighted association rule algorithm by incorporating dynamic time weights. It takes into account the user's intended time in the recommendation outcome. Moreover, it incorporated seasonal weights to optimize the weighted rule algorithm for factors such as user travel time and the attractions' peak season during the recommendation process. The experiment indicates that the F1 value of the improved algorithm model proposed in this study reaches 0.952, the accuracy reaches 0.985, the area under the curve reaches 0.955, the Recall value reaches 0.812, and the fitting degree reaches 0.971. The results suggest that the proposed cultural tourism attraction recommendation model, based on an optimized weighted association algorithm, performs well in recommending tourist destinations. This model can have a positive impact on the development of the tourism industry.