Abstract
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.
Highlights
With the continuous development of Internet technology and mobile device positioning technology, online travel planning system provides more and more travel convenience opportunities for customers [1]. e common recommendation system can be used as a way of information filtering, and it is an effective means to deal with the phenomenon of information overload. e system is based on the project model and user model, and its purpose is to find the project needed by the target user [2]
Context aware service is a key element in online travel planning system. e most widely used emotion analysis methods are machine learning and dictionary
According to different sources of sentiment dictionaries, it is divided into dynamic dictionary analysis method and existing dictionary analysis method [3]. rough machine learning, sentiment analysis methods are divided into naive Bayes, neural network, support vector machine, and other text sentiment analysis methods
Summary
Received 23 October 2021; Revised 14 November 2021; Accepted 18 November 2021; Published 7 December 2021. E key technology of online travel recommendation system has been widely concerned by many Internet experts. Is 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. E scores of the six emotional travel itinerary recommendation schemes are all higher than 142 points. E 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
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