A Novel Personalized Recommendation Model for Computing Advertising Based on User Acceptance Evaluation

Abstract

Nowadays, computing advertising has been an intelligent Internet application which provides personalized advertising service to customers. But how to suggest suitable advertising contents to users relies on effective mining of user preference characteristics. Conventionally, machine learning-based methods were most intuitive solutions to predict unknown user features. Nevertheless, such kind of approaches highly relied on massive labelled samples, and also cost much time in algorithm training. In realistic engineering application, running efficiency acts as the top priority. To deal with this issue, this paper proposes a novel personalized recommendation model for computing advertising based on user acceptance evaluation. Firstly, the functional requirements in personalized recommendation of computing advertising is analyzed, and the perceived behavior of the algorithm ethical risks generated in computing advertising is analyzed based on collaborative filtering. Then, based on the user experience risks generated in recommendation process, a user acceptance value for personalized recommendation is calculated. We also conduct some experiments on real-world data to make empirical assessment for the proposal. It can be concluded that recommendation effect of the proposal is better than that of machine learning algorithm and ant colony algorithm.

Keywords

• Personalized recommendation
• computing advertising
• user acceptance evaluation