Advanced Recommender Systems for Personalised E-Commerce
Аннотация
The spill-in effect of e-commerce advancement has made personalised recommendation systems important determinants of user interactivity, satisfaction, and sales conversions. However, although the traditional recommendation systems have provided a base of personalization, it has been limited in specificity due to data sparsity, cold-start issues, and context blindness. Advances in hybrid architectures, deep learning, sometimes attention, and reinforcement learning have been able to significantly enrich recommendation efforts. In this paper, the author presents an advanced hybrid recommender system architecture, which effectively combines collaborative filtering and content-based filtering with the deep sequential modelling mechanism and multi-modal context adaptation in order to provide highly customized recommendations. The measurement and testing of the proposed system are carried out on extensive e-commerce datasets to test the system in terms of accuracy, diversity, novelty, as well as customer satisfaction over various performance metrics. Findings prove that the hybrid model can produce a scaled and dynamic solution to the dynamic real-time use of personalization in a complex e-commerce environment, and this model is significantly superior to the conventional approaches. The framework handles the major issues in the industry, creating technical strength and being commercially viable.