The abundance of information on online purchasing websites makes it challenging for customers to locate products that match their preferences. However, the cold-start problem arises when there isn’t enough previous data, making it harder to make accurate recommendations for new customers or products. The enormous number of possible customers and products in a recommendation system leads to sparse data, which makes it harder to generate relevant recommendations and causes the sparsity problem. In addition, existing e-commerce recommender systems have difficulty making accurate product recommendations because they disregard individual consumer characteristics. In order to overcome these limitations, a hybrid recommender system combining ontology and sequential pattern mining (SPM) techniques is proposed. The strategy entails constructing an ontology that encompasses customer and product-related knowledge in the e-commerce domain. This ontology is then utilized to calculate customer preference similarities and generate predictions for the intended customer. The SPM algorithm is applied to the results of collaborative filtering to generate personalized recommendations for the customer. Experiments have demonstrated that the hybrid recommender system outperforms existing methods and resolves the cold-start problem and data sparsity in e-commerce recommender systems effectively. Even with limited initial data, the system generates accurate and individualized recommendations based on ontological domain knowledge and the customer’s sequential purchase patterns. By integrating ontology and sequential pattern mining, this strategy enhances the precision and individualization of the e-commerce industry’s recommendation process.

In this innovative study, a cutting-edge dual-channel closed-loop supply chain model with sensor data integration for third-party recycling has been effectively developed. Our innovative study provides unmatched insights into the world of consumer electronics by examining the effects of fund management regulations and retail e-commerce participation on three different choice scenarios. The compelling results show that retail e-commerce, the revolutionary force in the retail industry, has a tangible impact on the pricing and profits of traditional brick-and-mortar stores. In response to this trend, the wholesale pricing strategies of consumer electronics manufacturers have been adapted to maximize profitability within the retail e-commerce landscape. Fascinatingly, the impact of retail e-commerce involvement remains impervious to the recycling decisions and optimal profits of third-party recyclers. In consumer electronics, fund-handling policies are crucial in boosting the number of product recoveries and driving up prices for businesses in the forward supply chain. Although manufacturers’ market share is affected by retail e-commerce, overall product demand is unaffected. On the other hand, putting fund-handling policies into place indicates a decline in overall market demand. This innovative study uses sensor integration to unlock the complex dynamics of the interesting field of digital supply chain management.

Presents corrections to the paper, Corrections to “OntoCommerce: Incorporating Ontology and Sequential Pattern Mining for Personalized E-Commerce Recommendations”.