DLSF: Deep learning and semantic fusion based recommendation system

• Published in: Expert systems with applications 2024-09, Vol.250, p.123900, Article 123900
• Main Author: Bhatia, Vandana
• Format: Article
• Language: English
• Subjects: Collaborative filtering; Decision fusion; Deep learning; Recommendation system; Semantic analysis
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.123900

An intelligent recommendation system facilitates users by recommending items based on the user’s preferences. Most of the existing recommender systems employ collaborative filtering which consider homogenous data only and does not exploit different knowledge sources. Such approaches encounter many challenges such as Cold Start problem, Sparsity, and Scalability. To overcome these setbacks, a novel deep learning & semantic fusion-based recommendation system named DLSF is proposed in this paper which collaborates multiple knowledge sources by employing three modules to generate top-N recommendations. The first module precludes sparsity by using latent representations and deep learning for understanding user-item interactions. The second module exploits semantic information provided to similar items by the users for dealing with cold start problem. And the third module works for new users and items which don’t have any prior information. A comprehensive experimental evaluation is conducted over benchmark datasets. The proposed DLSF showed on average 16.02% and 16.71% more efficient over the baselines recommendation systems taken into account in terms of MAE and RMSE respectively. DLSF has found to have significant efficiency in terms of Precision, Recall and overall accuracy.