A recommendation system is an imaginative resolution for managing the restrictions in e-commerce services with item details and user details. Also, it is used to determine the user preferences to recommend the items they expected to buy. Several conventional collaborative filtering techniques are devised in the recommender model, but it has some complexities. Hence, an innovative optimization-driven deep residual network is devised in this paper for a product recommendation system. Here, the product of images is used for extracting features where the Convolutional neural network (CNN) features are computed, and then it is given as input to the deep residual network aimed at product recommendation. The deep residual network is trained using developed Elephant Herding Feedback Artificial Optimization (EHFAO), which is obtained by integrating Elephant Herding optimization (EHO) into the Feedback Artificial Tree (FAT). Here, the item grouping is carried out on input data based on K-means clustering. After item grouping, Cosine similarity is used to perform matching of groups, where the best group is acquired among all the available groups. Extraction of list of visitors is done from the best group. Then, the list of items is obtained from the sequence of best visitor. Next, the corresponding binary sequence is obtained for the applicable sequence of visitor. From this sequence of best visitor, the recommended product is acquired. Then, the recommended product is subjected to the sentiment analysis for which the score is determined. Here, the sentiment analysis helps to decide whether the product is recommended or not recommended. If the score is positive, then the same product is recommended; otherwise, the new product is recommended. The proposed EHFAO-based deep residual network attained better performance in comparison to the other techniques with a maximal F-measure at 84.061%, 84.061% precision, 87.845% recall along with minimal Mean Squared Error (MSE) of 0.216.

推荐系统是一种富有想象力的解决方案，用于管理具有项目详细信息和用户详细信息的电子商务服务中的限制。此外，它还用于确定用户偏好以推荐他们希望购买的商品。在推荐模型中设计了几种传统的协同过滤技术，但它具有一些复杂性。因此，本文为产品推荐系统设计了一种创新的优化驱动的深度残差网络。在这里，图像的乘积用于提取计算卷积神经网络 (CNN) 特征的特征，然后将其作为针对产品推荐的深度残差网络的输入。深度残差网络使用开发的大象放牧反馈人工优化 (EHFAO) 进行训练，这是通过将大象放牧优化 (EHO) 集成到反馈人工树 (FAT) 中获得的。这里，项目分组是基于 K-means 聚类对输入数据进行的。项目分组后，使用余弦相似度进行组匹配，在所有可用组中获取最佳组。从最佳组中提取访问者列表。然后，从最佳访问者的序列中获得项目列表。接下来，针对访问者的适用序列，得到对应的二进制序列。从这个最佳访问者序列中，获得推荐的产品。然后，对推荐产品进行确定分数的情绪分析。在这里，情绪分析有助于决定产品是否被推荐。如果得分为正，则推荐相同的产品；否则，建议使用新产品。与其他技术相比，所提出的基于 EHFAO 的深度残差网络获得了更好的性能，最大 F 度量为 84.061%，精度为 84.061%，召回率为 87.845%，最小均方误差 (MSE) 为 0.216。