Experimental Hybrid Approach for Improving the

Quality of Personalized Product Recommendation

Systems with Deep Learning

Deep learning has recently made significant advancements in recommender systems. Various methods,

including hybrid recommendation systems, content-based recommendations, and deep learning, have been employed to

enhance recommendation accuracy. Over the past decade, the proliferation of big data has led to a rapid increase in the

amount of available data on the internet. However, dealing with complex and large data sets makes it challenging for

many users to quickly find the information they need. At this juncture, recommendation systems play a crucial role in

addressing the issue of data overload. The development of recommendation algorithms has been particularly driven by

the growth of the e-commerce industry. Traditional single recommendation algorithms face challenges such as data

sparsity, long-tail items, and cold starts. Hybrid recommendation algorithms can effectively mitigate some of these

limitations. In response to these challenges, this paper proposes an experimental hybrid approach to improve the quality

of personalized product recommendations using deep learning, specifically an Improved Attention-Convolutional

Neural Network (IA-CNN). This method aims to address the shortcomings of single collaborative models. The

algorithm first employs a comprehensive approach, combining product- and user-based collaborative filtering strategies

to generalize and categorize the output results. This methodology captures detailed and abstract nonlinear interactions

between users and products through advanced deep learning techniques. Finally, experiments were designed to test the

algorithm's effectiveness. The IA-CNN algorithm was evaluated against a benchmark algorithm using the Amazon

product rating dataset. The results demonstrate that the proposed IA-CNN algorithm outperforms the benchmark in

rating prediction on the test dataset