ABSTRACT

In this work, we exploit a classifier based on convolutional neural network to predict users’ interests regarding products and services, according to their publications in social media. We aim at designing a customizable recommendation system to target social networks’ users as potential buyers. We evaluate the performance of training algorithms that use adaptive learning. The evaluation is performed in terms of the achieved accuracy rate to predict user’s interest. The selection result is backed up by the multi-criteria decision making. Two metrics are used to identify the real user interest profile of users based on publicly available photos.

中文翻译：

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在智能工厂环境中使用卷积神经网络进行高效推荐系统的客户档案预测

摘要

在这项工作中，我们利用基于卷积神经网络的分类器来预测用户对产品和服务的兴趣，根据他们在社交媒体上的出版物。我们的目标是设计一个可定制的推荐系统，将社交网络的用户作为潜在买家。我们评估使用自适应学习的训练算法的性能。评估是根据达到的准确率来执行的，以预测用户的兴趣。选择结果由多标准决策支持。根据公开的照片，使用两个指标来识别用户的真实用户兴趣概况。