A Hybrid Recommender System for Recommending Smartphones to Prospective Customers

TL;DR

This paper presents a hybrid recommender system combining ALS-based collaborative filtering with deep learning to improve smartphone recommendations and address cold start issues, outperforming existing systems.

Contribution

It introduces a novel hybrid architecture that integrates ALS and deep neural networks for enhanced recommendation accuracy and robustness.

Findings

Outperforms existing hybrid recommenders in accuracy

Effectively addresses cold start problem

Demonstrates improved recommendation quality in experiments

Abstract

Recommender Systems are a subclass of machine learning systems that employ sophisticated information filtering strategies to reduce the search time and suggest the most relevant items to any particular user. Hybrid recommender systems combine multiple recommendation strategies in different ways to benefit from their complementary advantages. Some hybrid recommender systems have combined collaborative filtering and content-based approaches to build systems that are more robust. In this paper, we propose a hybrid recommender system, which combines Alternating Least Squares (ALS) based collaborative filtering with deep learning to enhance recommendation performance as well as overcome the limitations associated with the collaborative filtering approach, especially concerning its cold start problem. In essence, we use the outputs from ALS (collaborative filtering) to influence the recommendations from a Deep Neural Network (DNN), which combines characteristic, contextual, structural and sequential information, in a big data processing framework. We have conducted several experiments in testing the efficacy of the proposed hybrid architecture in recommending smartphones to prospective customers and compared its performance with other open-source recommenders. The results have shown that the proposed system has outperformed several existing hybrid recommender systems.