Improved Personalized Recommendation Algorithm Based on Context-Aware in Mobile Computing Environment

Fei Long

doi:10.1155/2020/8857576

Abstract

With the development and popularization of e-commerce and Internet, more and more attention has been paid to personalized recommendation for users. The traditional user interest model only considers the user’s behavior on the project, ignoring the user’s context at that time. Pointing to the shortage that context-related factors are not considered in previous works, combining the characteristics of a mobile computing environment, this paper studies the algorithm and model of mobile service recommendation. A recommendation algorithm based on specified context filtering in mobile computing environment is proposed. The context of the classification is aggregated, by grouping the scenarios of the same category together. Through experiments, we found that the improved personalized recommendation algorithms are superior to the common collaborative filtering algorithm.

Highlights

The data included information of major websites and mobile APPs
The recommendation system came into being
The recommendation algorithm is the important part of the recommendation system [2]

Summary

Introduction

The data included information of major websites and mobile APPs. If the data had given a certain treatment, the information could have been more user-friendly and more efficient. In-depth research on the recommendation system allows users to get personalized recommendations, which greatly saved them time to search for information. The introduction of context information brings more ideas to improve the efficiency of the recommendation algorithm. The context information can bring more accurate recommendations to the users of the recommendation system. In this paper, combining the characteristics of the mobile computing environment, a recommendation algorithm based on specified context filtering in mobile computing environment is proposed.

Related Work

Conceptual Model of Context-Awareness

Context-Awareness Recommendation

Experiment Analysis

Summary and Future Work