Enhancing Recommender Systems to Alleviate Data Sparsity and the Cold Start Problem

The use of Collaborative Filtering (CF) in Recommender Systems for personalized suggestions on social websites and e-commerce platforms is prevalent, yet it faces challenges like sparsity, the Cold Start Problem (CSP), and a lack of explanation for recommendations. To tackle these issues, this paper introduces the novel deep ranking weighted multi-hashing Recommender (DRWMR) System. This system comprises two phases: neighbor formation and recommendation. In the neighbor formation phase, data is processed through a deep Convolutional Neural Network (CNN) to extract pertinent features and generate hash codes that assign weights to hash tables and bits. This weighted hamming distance facilitates the creation of user neighborhoods. In the recommendation phase, the system estimates ratings for unknown items by computing the weighted average rating of the neighborhood, leading to a list of top-n recommended items. The DRWMR system not only enhances recommendation precision and accuracy but also mitigates the challenges of sparsity and the Cold Start Problem. Experimental validation conducted on the MovieLens 100K dataset showcases the system's efficacy compared to existing methods. The evaluation metrics, including precision, recall, and F1 score, demonstrate the superiority of the proposed approach. Overall, the DRWMR system offers improved recommendation accuracy and the ability to provide reasoning behind the suggested items, addressing common challenges in CF-based recommender systems.