Improving Collaborative Filtering’s Rating Prediction Accuracy by Introducing the Common Item Rating Past Criterion

2019 ◽  
Author(s):  
Dionisis Margaris ◽  
Dionysios Vasilopoulos ◽  
Costas Vassilakis ◽  
Dimitris Spiliotopoulos
Keyword(s):  
Prediction Accuracy ◽  
Common Item ◽  
The Common ◽  
Rating Prediction

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

A Collaborative Filtering Recommendation Algorithm Based on Cloud Model, Item Attribute and User Data

2014 ◽  
Vol 610 ◽  
pp. 747-751
Author(s):  
Jian Sun ◽  
Xiao Ying Chen
Keyword(s):  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Cloud Model ◽  
Prediction Algorithm ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
User Data ◽  
Item Attribute ◽  
Rating Prediction ◽  
User Ratings

Aiming at the problems of extremely sparse of user-item rating data and poor recommendation quality, we put forward a collaborative filtering recommendation algorithm based on cloud model, item attribute and user data which combined with the existing literatures. A rating prediction algorithm based on cloud model and item attribute is proposed, based on idea that the similar users rating for the same item are similar and the same user ratings for the similar items are similar and stable. Through compare and analysis this paper’s and other studies experimental results, we get the conclusion that the rating prediction accuracy is improved.

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