A Novel Collaborative Filtering Algorithm Based on Social Network

2012 ◽  
pp. 164-174 ◽  
Author(s):  
Qun Liu ◽  
Yi Gao ◽  
Zhiming Peng
Keyword(s):  
Social Network ◽  
Collaborative Filtering ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

scholarly journals An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 68301-68310 ◽  
Author(s):  
Dionisis Margaris ◽  
Anna Kobusinska ◽  
Dimitris Spiliotopoulos ◽  
Costas Vassilakis
Keyword(s):  
Social Network ◽  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Rating Prediction

scholarly journals Recommending Locations Based on Users’ Periodic Behaviors

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Bing Xu ◽  
Zhijun Ding ◽  
Hongzhong Chen
Keyword(s):  
Time Series ◽  
Social Network ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
New Model ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Location Based Social Network ◽  
Gps Trajectory ◽  
Life Behavior

The research of location recommendation system is an important topic in the field of LBSN (Location-Based Social Network). Recently, more and more researchers began focusing on researching how to recommend locations based on user’s life behavior. In this paper, we proposed a new model recommending locations based on user’s periodic behaviors. In view of multiple periodic behaviors existing in time series, an algorithm which can mine all periods in time series is proposed in this paper. Based on the periodic behaviors, we recommend locations using item-based collaborative filtering algorithm. In this paper, we will also introduce our recommendation system which can collect users’ GPS trajectory, mine user’s multiple periods, and recommend locations based user’s periodic behavior.

scholarly journals A New Recommendation Algorithm Based on User’s Dynamic Information in Complex Social Network

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Jiujun Cheng ◽  
Yingbo Liu ◽  
Huiting Zhang ◽  
Xiao Wu ◽  
Fuzhen Chen
Keyword(s):  
Social Network ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Cold Start ◽  
Dynamic Information ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Response Information ◽  
Collaborative Filtering Algorithm ◽  
Information Dynamic

The development of recommendation system comes with the research of data sparsity, cold start, scalability, and privacy protection problems. Even though many papers proposed different improved recommendation algorithms to solve those problems, there is still plenty of room for improvement. In the complex social network, we can take full advantage of dynamic information such as user’s hobby, social relationship, and historical log to improve the performance of recommendation system. In this paper, we proposed a new recommendation algorithm which is based on social user’s dynamic information to solve the cold start problem of traditional collaborative filtering algorithm and also considered the dynamic factors. The algorithm takes user’s response information, dynamic interest, and the classic similar measurement of collaborative filtering algorithm into account. Then, we compared the new proposed recommendation algorithm with the traditional user based collaborative filtering algorithm and also presented some of the findings from experiment. The results of experiment demonstrate that the new proposed algorithm has a better recommended performance than the collaborative filtering algorithm in cold start scenario.

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