@Article{JICS-9-181,
author = {Waleed M. Al-Adrousy , Hesham A. Ali, and Taher T. Hamza},
title = {Linear Time Growth Collaborative Filtering Based on Caching Techniques},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {9},
number = {3},
pages = {181--188},
abstract = {One of social networks features is to recommend some items for users suitable and their profiles 
and needs.  One  of  the  used techniques  of  item  recommendation  is  to  calculate  the  average  rating  of  other 
users' ratings of that item. The calculated average can be used as a rank for that item. New users' ratings are 
made  within  the  lifetime  of  social  networks.  This  dynamic  sized  set  of  ratings  can  lead  to  a  performance 
problem.  Ranking  is  not  fixed  since  new  users  are  constantly  evaluating  items  in  networks.  However, 
updating ranking by traditional averaging can have a quadratic time growth. This paper aims to convert this 
aggregation method into linear growth instead of quadratic. The suggested algorithm needed extra storage 
capacity.  Caching is  suggested  to  speedup  ranking  with preserving  storage  resources.  Another  target  is  to 
have  a  high  hit-ratio  with  better  utilization  of  small  cache  size  compared  to  the  traditional  Greedy-  Dual-
Size-Frequency algorithm. At the end of this paper an evaluation of the proposed technique performance is 
provided based on simulated experimental results. The experiment results show that the proposed technique 
has better performance rather than traditional averaging recommender systems without caching. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22578.html}
}