A comparative study of various recommendation algorithms based on E-commerce big data

With the rapid development of the Internet and the concomitant exponential growth of information, we have entered an era characterized by information overload. The abundance of data has rendered it increasingly arduous for users to pinpoint specific information they require. However, various forms of recommendation algorithms proffer solutions to this challenge. These algorithms predict items or products that may pique users’ interest based on their historical behavior, preferences, and interests. As one of the current hot research fields, recommendation algorithms are extensively employed across E-commerce platforms, movie streaming services, and various other contexts to cater to the diverse needs of users. In this context, a multi-recommendation algorithms comparison platform is proposed, which includes a two-fold model: online evaluation and offline evaluation. Taking the data set of the Chinese Amazon online shopping mall as the experimental data, item-based collaborative filtering (Item-CF) algorithm, content-based (TF-IDF) algorithm, item2vec model, alternating least squares (ALS) algorithm and neural network algorithm are evaluated in the offline model. In the real-time recommendation part, model-based algorithm is used to achieve the users’ rating mechanism. And the metrics used for evaluation include: precision, recall, accuracy and performance. The experimental results show that the average performance of hybrid algorithms such as ALS algorithm and neural network algorithm is higher than that of other traditional algorithms, and the real-time recommendation system achieves the purpose of improving recommendation speed. By integrating various recommender algorithms into the multi-recommendation algorithms comparison platform, this platform automatically computes and presents various performance indicators based on the user-provided dataset. It aids E-commerce platforms in making informed decisions regarding algorithm selection. • A comprehensive recommendation algorithm comparison platform is proposed to explore the Merits and demerits of classical collaborative filtering algorithms and deep learning algorithms. For these different algorithms, the performance is tested from multiple indicators, such as: recall rate, accuracy rate, precision rate, calculation speed. • The computational workflow of the recommendation system comparison platform is crafted. Upon ingesting the dataset into the RACP, the user is assigned the responsibility of discerning the connotations and characteristics pertaining to each facet of the dataset. The system imports this data into recommendation algorithm models, subsequently evaluating the efficacy of each algorithm on that dataset based on dimension. • The visualization service is used to graphically represent the accuracy, recall rate and other performance indicators of each recommendation algorithm on different data sets of the system. In comparison to existing work focused on enhancing the accuracy of singular algorithms, the process of automatically comparing individual recommendation algorithms and presenting their respective performance metrics will furnish e-commerce platforms with more perspective, facilitating the discerning selection of recommended algorithms.