A tourist attraction recommendation algorithm based on the integration of user life cycle and adaptive time

This paper proposes a personalized recommendation algorithm that combines the weights of user lifetime and adaptive time in tourist attraction recommendations, aiming at the problems of dynamic changes in user interests, significant influence of time context, and data sparsity in collaborative filtering. First, users are divided into four life cycle stages - the exploration stage, the growth stage, the maturity stage, and the senior stage - based on the number of attractions they visit; Secondly, build a month-spot heat matrix and use time series smoothing techniques to enhance the stability of monthly heat; Then, on top of collaborative filtering, introduce a user lifecycle stage to adaptively fuse collaborative filtering scores with seasonal heat scores; Finally, experiments are conducted on real tourism datasets and compared with multiple baseline algorithms. The experiments demonstrated that the LST-Rec algorithm proposed in this paper outperformed traditional collaborative filtering algorithms in terms of accuracy, recall, coverage, and popularity metrics, with an accuracy of 39.19% and a recall of 56.60%. The ablation experiment verified the effectiveness of the adaptive fusion module, the method proposed in this study makes full use of the temporal context and user behavior patterns, effectively improving the accuracy and personalization level of tourist attraction recommendations, but provides a direction for subsequent research.