E-commerce recommender system based on improved K-means commodity information management model.

Since the start of the 21st century, there has been a rapid development of internet technology, causing electronic computers and smartphones to become increasingly popular. The e-commerce industry also experiences quick development. However, the recommendation technology of e-commerce progresses slowly, hindering it from keeping up with the changing times. To enhance the efficiency and accuracy of e-commerce recommender systems, this research introduces an e-commerce recommender system that utilizes an enhanced K-means clustering algorithm to manage commodity information. This method combines the K-means algorithm with a genetic algorithm by encoding the genetic algorithm, setting the initial population, defining the fitness function, and configuring other parameters. The results of the test indicated that the K-mean clustering algorithm and fuzzy C-mean algorithm had a recommendation accuracy of 87.9 % and 84.8 % respectively under the test dataset. The highest recommendation accuracy was observed from the improved K-mean clustering algorithm, which was 91.1 %. The convergence rate of the improved K-mean clustering algorithm was faster by 44 % compared to the traditional K-mean clustering algorithm and 73 % quicker than the fuzzy C-mean algorithm. The study's findings demonstrate that the refined K-means clustering algorithm greatly enhances the recommendation proficiency and precision of the e-commerce recommendation system, in comparison to other comparable algorithms. This research can potentially advance the e-commerce industry and stimulate its growth.

Camellia oleifera Abel shells as a new biosorbent to remove methylene blue from aqueous solutions.

This study aimed at investigating the feasibility of using Camellia oleifera Abel shell (COAS), an agriculture product in middle-west region in China, for the adsorption of methylene blue (MB), a cationic dye. Batch adsorption experiments were carried out to evaluate the effects of contact time, pH, adsorbent dosage, temperature and initial concentration on the adsorption of MB. Adsorption equilibrium studies showed that MB adsorption followed Langmuir model with a maximum adsorption capacity of 454.54 mg/g. The results demonstrated that the COAS is a promising adsorbent in the removal of MB from aqueous solutions.