An Improved Collaborative Filtering Recommendation Algorithm and Recommendation Strategy

Dong Li,Xiaofeng Li

doi:10.1155/2019/3560968

Abstract

The e-commerce recommendation system mainly includes content recommendation technology, collaborative filtering recommendation technology, and hybrid recommendation technology. The collaborative filtering recommendation technology is a successful application of personalized recommendation technology. However, due to the sparse data and cold start problems of the collaborative recommendation technology and the continuous expansion of data scale in e-commerce, the e-commerce recommendation system also faces many challenges. This paper has conducted useful exploration and research on the collaborative recommendation technology. Firstly, this paper proposed an improved collaborative filtering algorithm. Secondly, the community detection algorithm is investigated, and two overlapping community detection algorithms based on the central node and k-based faction are proposed, which effectively mine the community in the network. Finally, we select a part of user communities from the user network projected by the user-item network as the candidate neighboring user set for the target user, thereby reducing calculation time and increasing recommendation speed and accuracy of the recommendation system. This paper has a perfect combination of social network technology and collaborative filtering technology, which can greatly increase recommendation system performance. This paper used the MovieLens dataset to test two performance indexes which include MAE and RMSE. The experimental results show that the improved collaborative filtering algorithm is superior to other two collaborative recommendation algorithms for MAE and RMSE performance.

Highlights

As the amount of information continues to increase, the rapid development of Internet technology has brought us into the era of information explosion
Afterwards, this paper introduces the improved community detection algorithm and organically combined the community detection algorithm with the collaborative recommendation algorithm and studied the collaborative filtering recommendation algorithms based on the community detection
Experimental results show the Mean absolute error (MAE) and root mean square error (RMSE) values of the collaborative recommendation algorithm based on community detection are smaller than the other two algorithms

Summary

Introduction

As the amount of information continues to increase, the rapid development of Internet technology has brought us into the era of information explosion. It has been shown that the personalized recommendation based on the social network can solve the problem of data sparseness, new users, and new items in the traditional personalized recommendation. Based on the research of existing recommendation technologies, this paper proposes to introduce social network analysis technology into the recommendation system to realize a high efficiency and high scalability recommendation system to solve the problems of new users and new items [4]. From the perspective of recommendation system implementation, the memory-based collaborative recommendation is to load the user and product information into the internal memory and carry out real-time recommendations through the calculation [5,6,7] It is characterized by simple implementation and no training overhead [8, 9]. Afterwards, this paper introduces the improved community detection algorithm and organically combined the community detection algorithm with the collaborative recommendation algorithm and studied the collaborative filtering recommendation algorithms based on the community detection

Overlapping Community Detection Algorithm Based on Central Nodes

Overlapping Community Detection Algorithm Based on kFaction

Collaborative Filtering Recommendation

Experiment Design and Discussion

Conclusions