Abstract
This paper proposes a collaborative filtering algorithm based on user characteristics. The algorithm is mainly aimed at the problems of data sparsity and cold start in traditional collaborative filtering algorithm. First, through the analysis of user behavior, the time-interest weight function is introduced to improve the modified cosine similarity formula; then both the user preference degree and trust degree are introduced to improve the accuracy of the recommendation results. Finally, the experimental results based on hetrec2011 dataset show that the accuracy of the recommendation generated by the improved collaborative filtering algorithm is significantly improved compared with the traditional recommendation algorithm. The problems of data sparsity and cold start are effectively alleviated.
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.
