scholarly journals Study on Collaborative Filtering Algorithm Considering Temporal Variation of User Preference

2003 ◽  
Vol 13 (5) ◽  
pp. 526-529
Author(s):  
Young-Yong Park ◽  
Hak-Sung Lee
Keyword(s):  
Temporal Variation ◽  
Collaborative Filtering ◽  
User Preference ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

An effective collaborative filtering algorithm based on user preference clustering

2016 ◽  
Vol 45 (2) ◽  
pp. 230-240 ◽  
Author(s):  
Jia Zhang ◽  
Yaojin Lin ◽  
Menglei Lin ◽  
Jinghua Liu
Keyword(s):  
Collaborative Filtering ◽  
User Preference ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

scholarly journals A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

2021 ◽  
Vol 11 (12) ◽  
pp. 5416
Author(s):  
Yanheng Liu ◽  
Minghao Yin ◽  
Xu Zhou
Keyword(s):  
Collaborative Filtering ◽  
User Preference ◽  
Computation Method ◽  
Group Recommendation ◽  
Preference Rating ◽  
Filtering Method ◽  
Personal Recommendation ◽  
Collaborative Filtering Algorithm ◽  
Group Members ◽  
Individual Recommendation

The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.

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