NE-UserCF: Collaborative Filtering Recommender System Model based on NMF and E2LSH

Abstract

With the rapid development of big data and cloud computing, recommender systems (RSs) have gained significant attention in recent decades. However, there are still many challenges and drawbacks existed in RSs, such as complex and high-dimensional data, low recommendation accuracy, time-consuming and low-efficiency, which to a large extent restrict its applications. Non-negative Matrix Factorization algorithm (NMF) is a matrix factorization algorithm which finds the positive factorization of a given positive matrix. It can eliminate invalid and redundant features in user-rating matrix (URM), reduce URM's dimension. Exact Euclidean Locality Sensitive Hashing (E 2 LSH) is an advanced algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces. It can cluster similar-interest users (SIUs) of URM efficiently. Therefore, the authors propose an improved recommender system model named NE-UserCF (NMF-E 2 LSH-UserCF) based on NMF and E 2 LSH to improve the quality and performance of recommendation. The authors first utilize the NMF to process original URM, get a new-URM without invalid and redundant features. Then use E 2 LSH to cluster users in new-URM based on their interests and produce the similar-interest-user matrix (SIUM). The authors further process the Top-10 recommendations by adopting the user-based collaborative filtering algorithm (UserCF). Finally evaluate experimental results by analyzing metrics Precision, Recall, Coverage and Popularity. Experiments indicate that NE-UserCF proposed in this paper improves the quality of recommendation and has a good performance.

OCA: Ordered Clustering-Based Algorithm for E-Commerce Recommendation System

“…It uses hash functions to hash the items in high dimensional data sets and guarantees a high chance of collision for similar items and a low chance of collision for dissimilar ones. The work in [41] proposed a model with an improved LSH algorithm and a non-negative matrix factorization technique for user-based collaborative filtering to solve high dimensional data and low recommendation accuracy. Table 1 summarizes the analysis of the clustering algorithms proposed for e-commerce recommendation systems by the previous research works considered in this research work.…”

“…Clustering has been a rich subject of research in the area of recommendation systems and numerous clustering algorithms have been proposed in the literature [31]. This includes, but is not limited to, K-Means [30,32,33], Fuzzy C-Means [34][35][36], Bi-Clustering [37], Evolutionary Clustering [38][39][40], and Locality Sensitive Hashing [41].…”