DeepRec: Efficient Product Recommendation Model for E-Commerce using CNN

E-commerce, which provides access to millions of things online, has exploded in popularity in recent years. The availability of several options may cause shoppers' heads to spin and make it harder for them to settle on a single purchase. In recent years, many papers proposed the recommendation system as the potential solution to these issues. Hence, this study constructs a novel recommendation model using a learning technique to build an efficient recommender for e-commerce products. To set up our model, we compile a sizable e-commerce dataset. According to our experiments, our technique improves accuracy by 91.47 percent on the training set and 91.17 percent on the testing set. For this reason, the approach we present has the potential to be an effective means of resolving recommendation-related challenges in the actual implementation of e-commerce.