Hybrid Recommendation System for Online Shopping of Electronic Product using Advanced Association rule mining

Abstract

Providing advice to people is now a thing of the past. Users have an important influence in user retention. Individual buying experiences are being improved. Online Shopping of Electronic Product involves the of commodities, it is subject to a variety of policies and regulations, resulting in some unique requirements for the recommendation system, making the traditional collaborative filtering recommendation algorithm less effective for cross-border Online Shopping of Electronic Product. This research proposes a simple yet effective Online Shopping of Electronic Product personalised recommendation model that incorporates association rule mining and complex preference into a recommendation model to address this issue. To mine user preference features, a hybrid recommendation model based on user complex preference features is created under the constraint of association rules mining, and individualised commodities recommendations are realised based on user behaviour choice. The enhanced method lowers the impact of data sparsity when compared to the traditional recommendation algorithm.