Approaching the cold-start problem using community detection based alternating least square factorization in recommendation systems

• Published in: Evolutionary intelligence 2021-06, Vol.14 (2), p.835-849
• Main Authors: Paleti, Lakshmikanth, Radha Krishna, P., Murthy, J. V. R.
• Format: Article
• Language: English
• Subjects: Algorithms; Applications of Mathematics; Artificial Intelligence; Bioinformatics; Cold starts; Collaboration; Control; Engineering; Filtration; Least squares; Mathematical and Computational Engineering; Mechatronics; Recommender systems; Robotics; Singular value decomposition; Special Issue; Statistical Physics and Dynamical Systems
• ISSN: 1864-5909; 1864-5917
• DOI: 10.1007/s12065-020-00464-y

In e-commerce, the opinion of users about products and the reviews are identified using recommender systems. Collaborative filtering techniques are popularly used techniques for giving recommendations to the users. One of the common challenges in the collaborative filtering technique for giving recommendations is cold start problem, which occurs due to insufficient information about new items and new users. This paper proposes a hybrid approach entitled LA-ALS to address the cold start problem to provide effective recommendations. The LA-ALS approach makes use of the benefits of both Louvain’s algorithm and alternating least square algorithm. The Louvain’s algorithm is used to analyze the relationship between users and alternating least square algorithm is used to predict recommendations. Experiments are carried out by using real-world datasets such as Movielens and Facebook databases. The effectiveness of the LA-ALS approach is shown with two parameters namely mean absolute error and root mean square error. The results showed that LA-ALS approach generated better recommendations when compared with the existing techniques such as k-nearest neighbors and singular value decomposition.