Personalized product recommendations based on deep learning
Jiachang He · Applied and Computational Engineering · 2024
Offline commodity sales efficiency is low, which brings a lot of inconvenience to people. Fortunately, with the progress of information technology, online shopping platforms have become popular. Upon accessing shopping platforms, myriad products spanning household appliances, groceries, apparel, and electronics become instantly available. The variety of goods on the online shopping platform can’t be directly contacted, which increases the difficulty of the user's choice of goods. In addition, it is difficult for online merchants to recommend suitable products to customers in the way of offline communication. To solve this problem, this paper proposes a product recommendation model based on deep learning. This model uses consumer preferences, historical shopping data, and product characteristics to calculate personalized recommendation scores. Such scores expediently guide consumers to products that align with their specific requirements, enhancing the overall purchasing experience. At the same time, it also improves the sales efficiency of merchants.