Optimizing Parallel Collaborative Filtering Approaches for Improving Recommendation Systems Performance

Abstract

Recommender systems are one of the fields of information filtering systems that have attracted great research interest during the past several decades and have been utilized in a large variety of applications, from commercial e-shops to social networks and product review sites. Since the applicability of these applications is constantly increasing, the size of the graphs that represent their users and support their functionality increases too. Over the last several years, different approaches have been proposed to deal with the problem of scalability of recommender systems’ algorithms, especially of the group of Collaborative Filtering (CF) algorithms. This article studies the problem of CF algorithms’ parallelization under the prism of graph sparsity, and proposes solutions that may improve the prediction performance of parallel implementations without strongly affecting their time efficiency. We evaluated the proposed approach on a bipartite product-rating network using an implementation on Apache Spark.

Latent based temporal optimization approach for improving the performance of collaborative filtering

“…The primary idea of the CF approach is that if a user (say X) shares an attitude with another user (Y) on a subject, X is more likely to share Y's attitude on a different issue when compared to other randomly chosen users. CF is one of the most implemented techniques used in the design of recommendation systems due to its low computational requirement (Jonnalagedda et al, 2016;Sardianos, Ballas Papadatos & Varlamis, 2019;Alhijawi & Kilani, 2020).…”

“…Recommendation systems are some of the most powerful methods for suggesting products to customers based on their interests and online purchases (Jonnalagedda et al, 2016;Lin, Li & Lian, 2020;Nilashi, bin Ibrahim & Ithnin, 2014;Nilashi et al, 2015;Zhang et al, 2020b). In terms of personalization of recommendations, one of the most prevalently used methods is collaborative filtering (CF) (Nilashi, bin Ibrahim & Ithnin, 2014;Sardianos, Ballas Papadatos & Varlamis, 2019;Nilashi et al, 2015;Wu et al, 2019). In CF, personalized prediction of products depends on the latent features of users in a rating matrix.…”

Peer Review #3 of "Latent based temporal optimization approach for improving the performance of collaborative filtering (v0.1)"

“…CF is one of the most implemented techniques used in the design of recommendation systems due to its low computational requirement (Jonnalagedda et al, 2016;Sardianos et al, 2019;Alhijawi and Kilani, 2020). It utilizes to find similar users or items and calculate predicted rating scores according to ratings of similar users.…”

Peer Review #1 of "Latent based temporal optimization approach for improving the performance of collaborative filtering (v0.1)"

“…Recommendation systems are some of the most powerful methods for suggesting products to customers based on their interests and online purchases (Jonnalagedda et al, 2016;Lin et al, 2020;Nilashi et al, 2014Nilashi et al, , 2015Zhang et al, 2020b). In terms of personalization of recommendations, one of the most prevalently used methods is collaborative filtering (CF) (Nilashi et al, 2014;Sardianos et al, 2019;Nilashi et al, 2015;Wu et al, 2019). In CF, personalized prediction of products depends on the latent features of users in a rating matrix.…”