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.

I. Introduction

In the modern digital landscape, recommendation systems (RS) have gained substantial prominence across diverse platforms, spanning domains such as e-commerce, movies, music, and television programs [1]. These systems capitalize on user data to offer tailored suggestions, enriching user experiences and aiding in decision-making processes [2]. Various RS techniques have emerged to predict user behavior and enhance recommendation quality [3] [4]. Typically, RS functions by analyzing individual user preferences and historical usage patterns to identify highly favored items [5]. This application of RS serves as a potent tool for machine learning, substantially amplifying product sales [6] [7]. Recommendations not only streamline the user search process but also introduce them to relevant content and offers that might have remained undiscovered [8] [9]. Moreover, businesses can effectively engage and retain customers by presenting movies and TV shows tailored to their user profiles [10]. RS methodologies broadly fall into categories such as collaborative filtering (CF), content-based (CB), and hybrid systems, depending on the data used for recommendations [11]. CB filtering, frequently employed in RS design, uses item attributes to derive characteristics that align with user preferences [12]. In contrast, CF systems function based on user-item relationships. Hybrid systems aim to leverage both types of data to address challenges that arise from relying solely on one method. Despite the proven benefits of RS across various domains, certain challenges persist, notably the sparsity problem and the cold start problem (CSP). The sparsity problem emerges when users do not rate or provide feedback on items during their online interactions, resulting in an insufficient number of available ratings. This poses a challenge for CF methods that heavily rely on rating matrices. The CSP arises with the introduction of a new product into the RS, lacking previous ratings, which affects the accuracy of recommendations, especially for new users. Researchers have made endeavors to develop effective and well-structured RS algorithms to tackle these challenges. Existing methods, such as Local Sensitive Hashing (LSH) [13], Bayesian Personalized Ranking (BPR) [14], and Cofi Rank-maximum Margin Matrix Factorization (CRMF) [15], have been utilized to alleviate sparsity and the CSP. However, these approaches often struggle to produce deep hashing that simultaneously minimizes classification loss, pairwise ranking, and storage costs. Hence, there is a necessity for an efficient method to recommend items that effectively overcomes these limitations. This study introduces a novel approach to address the challenges of sparsity and the CSP in RS. Our proposed method emphasizes the generation of deep hashing by minimizing classification loss and pairwise ranking while mitigating excessive storage costs. By employing this approach, we aim to enhance the precision and efficiency of item recommendations. We conduct extensive experiments to assess the efficacy of our method and compare it with existing approaches. The results highlight the superiority of our proposed method in terms of recommendation performance and storage efficiency, emphasizing its potential to enhance RS in diverse domains.