Improving Complementary-Product Recommendations Using Deep Neural Networks

Abstract

This research inspects the integration of Deep Neural Networks (DNNs) into recommendation systems, focusing on improving the personalization and accuracy of complementary-product suggestions in e-commerce. Study begins with thoroughly examining existing methodologies in recommendation systems, including conventional approaches like content-based and collaborative filtering also advanced techniques involving neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). These technologies are analyzed for their effectiveness in comprehending and predicting complex user-item interactions more efficiently than traditional methods.
To implement and assess these advanced methodologies, the study leverages the 'LightFM' library to extract and preprocess user rating data from the MovieLens dataset. It concentrates on high-rating interactions to ensure the model focuses on positive user experiences. The preprocessing steps involve data cleansing, normalization, and conversion of ratings into a binary format, simplifying the neural network's training and prediction processes.
A critical aspect of this research involves developing a customized DNN model specifically designed to recommend complementary products. This model is carefully trained and evaluated, and its architecture allows for both non-linear and linear interpretation between user as well as item data, capturing intricate patterns that reflect genuine user preferences. The model's performance is rigorously tested against traditional recommendation systems, highlighting its superior ability in terms of user satisfaction, scalability, and accuracy.
Research indicates that DNNs significantly enhance recommendation quality by providing more personalized and dynamic suggestions compared to conventional models. The adaptability of DNNs to changing user behavior’s and preferences demonstrates their potential to support real-time and responsive recommendation systems. For e-commerce businesses, this translates to increased customer engagement, higher retention rates, and an overall improvement in user experience.
Overall, research advances academic understanding of DNN applications in recommendation systems. It offers practical insights for e-commerce practitioners seeking advanced machine learning technologies to refine their marketing strategies and product offerings. The continued integration of deep learning could redefine personalization and efficiency in digital shopping environments, promising a bright future for recommendation systems.