The key technology of online travel recommendation system has been widely concerned by many Internet experts. This paper studies and designs a scenario aware service model in online travel planning system and proposes an online travel planning recommendation model which integrates collaborative filtering and clustering personalized recommendation algorithm. At the same time, the algorithm performance test method and model evaluation index are given. The results show that CTTCF algorithm can find more neighbor users than UCF algorithm, and the smaller the search space is, the more significant the advantage is. The number of neighbors is 5, 10, 15, 20, and 25, respectively, and the corresponding average absolute error values are about 0.815, 0.785, 0.765, 0.758, and 0.755, respectively. The scores of the six emotional travel itinerary recommendation schemes are all higher than 142 points. Only the two schemes have no obvious rendering effect. The proposed online travel itinerary planning scheme has potential value and important significance in the application of follow-up recommendation system. It solves the problem of low scene perception satisfaction in the key technologies of online tourism planning system.
Learning Adaptive Recommendation Strategies for Online Travel Planning
