A Study on Hybrid Recommender Systems for Effective Targeted Marketing in E-Commerce Platforms

Abstract

In the rapidly evolving world of digital commerce, offering tailored user experiences has emerged as a key factor in driving long-term success and staying ahead of the competition. With users generating vast amounts of behavioural data across various digital channels, e-commerce platforms face the dual challenge of interpreting this data effectively and translating it into actionable marketing strategies. Recommender systems have proven instrumental in this regard, offering predictive insights into consumer preferences. Conventional recommendation techniques, including collaborative filtering and content-based approaches, often struggle with limitations such as sparse data availability, cold-start problems, and a lack of contextual depth when used in isolation. To overcome these barriers, hybrid recommender systems have emerged as a robust solution, integrating multiple algorithmic strategies to deliver more precise, varied, and scalable personalized suggestions. This study investigates the application of hybrid recommendation models within targeted marketing frameworks in e-commerce. It examines various hybridization techniques, such as weighted, mixed, and switching models, and their effectiveness in tailoring product suggestions to user behaviour patterns. By analysing real-world e-commerce data, the research examines essential performance indicators such as click-through rates, conversion metrics, and the overall lifetime value of customers. Moreover, the research explores how insights from hybrid systems can be integrated into campaign automation tools, creating adaptive feedback loops for marketing optimization. Beyond algorithmic performance, the study addresses critical concerns including user privacy, algorithmic interpretability, and ethical personalization. The role of explainable AI (XAI) in enhancing user trust and regulatory compliance is also examined. Ultimately, this work offers a holistic framework for leveraging hybrid recommender systems to build responsive, user-centric digital commerce strategies.