Abstract
With the rapid development of China’s economy, people pay attention to their own quality of life, and tourism has become the first choice for people from all walks of life to relax themselves. Tourism travel has mainly developed from the form of travel agency registration to the form of online registration based on the network platform business model. Considering the value cocreation and the diversity of tourism enterprise platform, this paper puts forward the business model research of intelligent recommendation of tourism enterprise platform from the perspective of value cocreation. Firstly, the commonly used recommendation algorithms are introduced, which are collaborative filtering recommendation algorithm, content filtering recommendation algorithm, and association rule recommendation algorithm. Secondly, it analyzes the number of tourists and economic benefits of the business platform of tourism enterprises from April 2020 to April 2021 and also analyzes the business models of five modules under the tourism platform on different platforms. Finally, three recommendation algorithms are used to compare the comprehensive performance of five modules in different business models. Finally, we find that the rate of accuracy and recall of business is above 88%, which can have good economic benefits and provide customers with high-quality recommendation service and good satisfaction.
Highlights
Input: current user ID, user-item scoring matrix UR, number of nearest neighbors K, maximum recommended value top-N; Output: Recommended item list L; Step 1: in the recommendation system based on user CF, input the scoring data of all users for all items, and construct a matrix R of M ∗ N as shown in Table 1 (M users and N items), and Rij represents the scoring value of the ith user for the jth item. e rating of a user is considered as a vector in the n-dimensional term space
Collaborative Filtering Recommendation Algorithm. e core part of collaborative filtering recommendation algorithm is the selection of nearest neighbors [13]. e effectiveness and efficiency of this algorithm depend on the effectiveness and efficiency of neighbor user similarity calculation. ere are three common calculation methods: cosine similarity, modified cosine similarity, and Pearson correlation coefficient, which will be introduced below
Considering the commercial value, the service value platform provided by tourism enterprises provides customers with high-quality services and gains the satisfaction of users in order to realize commercial value. e three service recommendation algorithms proposed in this paper are applied to the business service recommendation of tourism platform
Summary
E core part of collaborative filtering recommendation algorithm is the selection of nearest neighbors [13]. E effectiveness and efficiency of this algorithm depend on the effectiveness and efficiency of neighbor user similarity calculation. Ere are three common calculation methods: cosine similarity, modified cosine similarity, and Pearson correlation coefficient, which will be introduced below. E basic steps of collaborative filtering recommendation algorithm are as follows. E modified cosine similarity calculation method subtracts the user’s average score for all items from the user’s score for a certain item, which avoids the calculation method of formula (1) not considering the difference between the average user scores. Some users tend to score high scores and low scores, thereby solving the problem of scoring scale among different users, and the calculation formula is defined. Ri and Rj represent the average scores of users i and j on the project, respectively
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
