Abstract
Bipartite graph networks are widely used as an analytical method for complex networks. Aiming at the cold start problem of the traditional recommendation algorithm, this paper proposes to combine the bipartite graph recommendation algorithm with the random walk algorithm, and then add the geographical influence factor to improve the effect of O2O ecommerce personalized recommendation. The experiment was conducted by selecting 45,974 users in the Yelp data set as an example. The online review was used as a weight to construct a user-business bipartite graph network and found to have a small world feature. The experimental results show that the combination of the random walk algorithm can make recommendations accurately, and add the characteristics of localization, by comparing the cities of the businesses, can be more in line with the actual situation. The accuracy of the final model algorithm is improved, which verifies the effectiveness of the model.
Sign-in/Register to access full text options 
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.
