TY - GEN
T1 - A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques
AU - Zarzour, Hafed
AU - Al-Sharif, Ziad
AU - Al-Ayyoub, Mahmoud
AU - Jararweh, Yaser
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/4
Y1 - 2018/5/4
N2 - With the advent and explosive growth of the Web over the past decade, recommender systems have become at the heart of the business strategies of e-commerce and Internet-based companies such as Google, YouTube, Facebook, Netflix, LinkedIn, Amazon, etc. Hence, the collaborative filtering recommendation algorithms are highly valuable and play a vital role at the success of such businesses in reaching out to new users and promoting their services and products. With the aim of improving the recommendation performance of such an algorithm, this paper proposes a new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. The k-means algorithm and Singular Value Decomposition (SVD) are both used to cluster similar users and reduce the dimensionality. It proposes and evaluates an effective two stage recommender system that can generate accurate and highly efficient recommendations. The experimental results show that this new method significantly improves the performance of the recommendation systems.
AB - With the advent and explosive growth of the Web over the past decade, recommender systems have become at the heart of the business strategies of e-commerce and Internet-based companies such as Google, YouTube, Facebook, Netflix, LinkedIn, Amazon, etc. Hence, the collaborative filtering recommendation algorithms are highly valuable and play a vital role at the success of such businesses in reaching out to new users and promoting their services and products. With the aim of improving the recommendation performance of such an algorithm, this paper proposes a new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. The k-means algorithm and Singular Value Decomposition (SVD) are both used to cluster similar users and reduce the dimensionality. It proposes and evaluates an effective two stage recommender system that can generate accurate and highly efficient recommendations. The experimental results show that this new method significantly improves the performance of the recommendation systems.
KW - Collaborative filtering recommendation algorithm
KW - SVD
KW - clustering
KW - dimension reduction
KW - recommender systems
UR - https://www.scopus.com/pages/publications/85046545522
U2 - 10.1109/IACS.2018.8355449
DO - 10.1109/IACS.2018.8355449
M3 - Conference contribution
AN - SCOPUS:85046545522
T3 - 2018 9th International Conference on Information and Communication Systems, ICICS 2018
SP - 102
EP - 106
BT - 2018 9th International Conference on Information and Communication Systems, ICICS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Information and Communication Systems, ICICS 2018
Y2 - 3 April 2018 through 5 April 2018
ER -